References

Aakvik, Arild, James J. Heckman, and Edward J. Vytlacil. 2005. “Estimating Treatment Effects for Discrete Outcomes When Responses to Treatment Vary: An Application to Norwegian Vocational Rehabilitation Programs.” Journal of Econometrics 125 (1-2): 15–51. https://doi.org/10.1016/j.jeconom.2004.04.003.

Abadie, Alberto. 2005. “Semiparametric Difference-in-Differences Estimators.” Review of Economic Studies 72 (1): 1–19. https://doi.org/10.1111/0034-6527.00321.

Abadie, Alberto, Joshua D. Angrist, and Guido W. Imbens. 2002. “Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings.” Econometrica 70 (1): 91–117. https://doi.org/10.1111/1468-0262.00271.

Acemoglu, D., S. Johnson, and J. Robinson. 2001. “The Colonial Origins of Comparative Development: An Empirical Investigation.” The American Economic Review 91 (5): 1369–1401.

Albert, J. H., and S. Chib. 1993. “Bayesian Analysis of Binary and Polychotomous Response Data.” Journal of the American Statistical Association 88 (422): 669–79.

Albert, Jim. 2009. Bayesian Computation with r. 2nd ed. Use r! New York, NY: Springer. https://doi.org/10.1007/978-0-387-92297-3.

Alonzo, Izhar Asael, and Cristian Cruz. 2020. “varstan: An R Package for Bayesian Time Series Models with Stan.” ARXIV Preprint.

An, Sungbae, and Frank Schorfheide. 2007. “Bayesian Analysis of DSGE Models.” Econometric Reviews 26 (2-4): 113–72.

An, Ziwen, David J Nott, and Christopher Drovandi. 2020. “Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach.” Statistics and Computing 30 (3): 543–57.

An, Ziwen, Leah F South, and Christopher Drovandi. 2022. “BSL: An r Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood.” Journal of Statistical Software 101: 1–33.

An, Ziwen, Leah F South, David J Nott, and Christopher C Drovandi. 2019. “Accelerating Bayesian Synthetic Likelihood with the Graphical Lasso.” Journal of Computational and Graphical Statistics 28 (2): 471–75.

Andrews, Isaiah, and Anna Mikusheva. 2022. “Optimal Decision Rules for Weak GMM.” Econometrica 90 (2): 715–48. https://doi.org/10.3982/ECTA18678.

Andrieu, C., A. Doucet, and R. Holenstein. 2010. “Particle Markov Chain Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (3): 269–342.

Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. 2010. “Particle Markov Chain Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (3): 269–342. https://doi.org/10.1111/j.1467-9868.2009.00736.x.

Andrieu, Christophe, and Gareth O. Roberts. 2009. “The Pseudo-Marginal Approach for Efficient Monte Carlo Computations.” Annals of Statistics 37 (2): 697–725. https://doi.org/10.1214/07-AOS574.

Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91 (434): 444–55. https://doi.org/10.2307/2291629.

Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton University Press.

———. 2010. “Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009).” Journal of Economic Literature 48 (2): 399–423. https://doi.org/10.1257/jel.48.2.399.

———. 2014. Mastering ’Metrics: The Path from Cause to Effect. Princeton, NJ: Princeton University Press.

Anselin, Luc. 1982. “A Note of Small Sample Properties of Estimators in a First Order Spatial Autoregressive Model.” Environment and Planning 14: 1023–30.

Antoniak, C. 1974. “Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems.” The Annals of Statistics 2 (6): 1152–74.

Baath, R. 2018. Package Bayesboot. https://CRAN.R-project.org/package=bayesboot.

Baker, Andrew, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H. C. Sant’Anna. 2025. “Difference-in-Differences Designs: A Practitioner’s Guide.” arXiv Preprint arXiv:2503.13323, June.

Baker, Jack, Paul Fearnhead, Emily B Fox, and Christopher Nemeth. 2019. “Sgmcmc: An r Package for Stochastic Gradient Markov Chain Monte Carlo.” Journal of Statistical Software 91: 1–27.

Barbieri, M., and J. Berger. 2004. “Optimal Predictive Model Selection.” The Annals of Statistics 32 (3): 870–97.

Bardenet, Rémi, Arnaud Doucet, and Chris Holmes. 2014. “Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approach.” In International Conference on Machine Learning, 405–13. PMLR.

———. 2017. “On Markov Chain Monte Carlo Methods for Tall Data.” Journal of Machine Learning Research 18 (47): 1–43.

Bartlett, Peter L, Philip M Long, Gábor Lugosi, and Alexander Tsigler. 2020. “Benign Overfitting in Linear Regression.” Proceedings of the National Academy of Sciences 117 (48): 30063–70.

Basu, Sanjib, and Siddhartha Chib. 2003. “Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models.” Journal of the American Statistical Association 98 (461): 224–35.

Bayarri, M. J., and J. Berger. 2004. “The Interplay of Bayesian and Frequentist Analysis.” Statistical Science 19 (1): 58–80.

Bayarri, M., and J. Berger. 2000. “P–Values for Composite Null Models.” Journal of American Statistical Association 95: 1127–42.

Bayes, T. 1763. “An Essay Towards Solving a Problem in the Doctrine of Chances.” Philosophical Transactions of the Royal Society of London 53: 370–416.

Bayes, Thomas. 1763. “LII. An Essay Towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, FRS Communicated by Mr. Price, in a Letter to John Canton, AMFR S.” Philosophical Transactions of the Royal Society of London, no. 53: 370–418.

Beaumont, Mark A, Wenyang Zhang, and David J Balding. 2002. “Approximate Bayesian Computation in Population Genetics.” Genetics 162 (4): 2025–35.

Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019. “Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off.” Proceedings of the National Academy of Sciences 116 (32): 15849–54.

Benjamin, Daniel J, James O Berger, Magnus Johannesson, Brian A Nosek, E-J Wagenmakers, Richard Berk, Kenneth A Bollen, et al. 2018. “Redefine Statistical Significance.” Nature Human Behaviour 2 (1): 6–10.

Berger, J. 1993. Statistical Decision Theory and Bayesian Analysis. Third Edition. Springer.

———. 2006. “The Case for Objective Bayesian Analysis.” Bayesian Analysis 1 (3): 385–402.

Berger, James O. 2013. Statistical Decision Theory and Bayesian Analysis. Springer Science & Business Media.

Berger, James O, and Luis R Pericchi. 1996. “The Intrinsic Bayes Factor for Model Selection and Prediction.” Journal of the American Statistical Association 91 (433): 109–22.

Bernardo, José M, and Adrian FM Smith. 2009. Bayesian Theory. Vol. 405. John Wiley & Sons.

Bernardo, J., and A. Smith. 1994. Bayesian Theory. Chichester: Wiley.

Bertorelle, Giorgio, Andrea Benazzo, and S Mona. 2010. “ABC as a Flexible Framework to Estimate Demography over Space and Time: Some Cons, Many Pros.” Molecular Ecology 19 (13): 2609–25.

Bickel, Peter J, and Joseph A Yahav. 1969. “Some Contributions to the Asymptotic Theory of Bayes Solutions.” Zeitschrift für Wahrscheinlichkeitstheorie Und Verwandte Gebiete 11 (4): 257–76.

Bishop, Christopher M, and Nasser M Nasrabadi. 2006. Pattern Recognition and Machine Learning. Springer.

Bissiri, Pier Giovanni, Chris C Holmes, and Stephen G Walker. 2016. “A General Framework for Updating Belief Distributions.” Journal of the Royal Statistical Society Series B: Statistical Methodology 78 (5): 1103–30.

Bivand, Roger, Virgilio Gómez-Rubio, and Håvard Rue. 2015. “Spatial Data Analysis with r-INLA with Some Extensions.” Journal of Statistical Software 63: 1–31.

Blackwell, D., and J. MacQueen. 1973. “Ferguson Distributions via Pólya Urn Schemes.” The Annals of Statistics 1: 353–55.

Blei, David M, and Michael I Jordan. 2006. “Variational Inference for Dirichlet Process Mixtures.” Bayesian Analysis 1 (1): 121–43.

Blei, David M, Alp Kucukelbir, and Jon D McAuliffe. 2017. “Variational Inference: A Review for Statisticians.” Journal of the American Statistical Association 112 (518): 859–77.

Blum, Michael GB. 2010. “Approximate Bayesian Computation: A Nonparametric Perspective.” Journal of the American Statistical Association 105 (491): 1178–87.

Bollerslev, Tim. 1986. “Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics 31 (3): 307–27. https://doi.org/10.1016/0304-4076(86)90063-1.

Bondell, Howard D., and Brian J. Reich. 2008. “Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR.” Biometrics 64 (1): 115–23. https://doi.org/10.1111/j.1541-0420.2007.00843.x.

Box, G. E. P. 1976. “Science and Statistics.” Journal of the American Statistical Association 71: 791–99.

Box, George E. P. 1976. “Science and Statistics.” Journal of the American Statistical Association 71 (356): 791–99. https://doi.org/10.1080/01621459.1976.10480949.

Box, George E. P., and Gwilym M. Jenkins. 1976. Time Series Analysis: Forecasting and Control. 1st ed. San Francisco: Holden-Day.

Box, George EP. 1979. “Robustness in the Strategy of Scientific Model Building.” In Robustness in Statistics, 201–36. Elsevier.

Branson, Zach, Maxime Rischard, Luke Bornn, and Luke W. Miratrix. 2019. “A Nonparametric Bayesian Methodology for Regression Discontinuity Designs.” Journal of Statistical Planning and Inference 202: 14–30. https://doi.org/10.1016/j.jspi.2019.01.003.

Breiman, Leo. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–40.

———. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.

Breiman, Leo, Jerome H Friedman, Richard A Olshen, and Charles J Stone. 1984. Classification and Regression Trees. Belmont, CA: Wadsworth International Group.

Breunig, Christoph, Ruixuan Liu, and Zhengfei Yu. 2025. “Double Robust Bayesian Inference on Average Treatment Effects.” Econometrica 93 (2): 539–68. https://doi.org/10.3982/ECTA21442.

Brochu, Eric, Vlad M. Cora, and Nando de Freitas. 2010. “A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.” arXiv Preprint arXiv:1012.2599.

Burnham, Kenneth P, and David R Anderson. 2004. “Multimodel Inference: Understanding AIC and BIC in Model Selection.” Sociological Methods & Research 33 (2): 261–304.

Cameron, Colin, and Pravin Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge.

Cappé, O., S. J. Godsill, and E. Moulines. 2007. “An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo.” Proceedings of the IEEE 95 (5): 899–924.

Cappé, Olivier, Simon J Godsill, and Eric Moulines. 2007. “An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo.” Proceedings of the IEEE 95 (5): 899–924.

Carlin, Bradley P, Alan E Gelfand, and Adrian FM Smith. 1992. “Hierarchical Bayesian Analysis of Changepoint Problems.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 41 (2): 389–405.

Carpenter, Bob, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “Stan: A Probabilistic Programming Language.” Journal of Statistical Software 76: 1–32.

Carpenter, J., P. Clifford, and P. Fearnhead. 1999. “Improved Particle Filter for Nonlinear Problems.” IEE Proceedings-Radar, Sonar and Navigation 146 (1): 2–7.

Carter, Chris K, and Robert Kohn. 1994. “On Gibbs Sampling for State Space Models.” Biometrika 81 (3): 541–53.

Casella, George, and Roger Berger. 2024. Statistical Inference. CRC Press.

Casella, G., and E. Moreno. 2006. “Objective Bayesian Variable Selection.” Journal of the American Statistical Association 101 (473): 157–67.

Cattaneo, Matias D., Nicolas Idrobo, and Rocı́o Titiunik. 2019. “A Practical Introduction to Regression Discontinuity Designs: Foundations.” Cambridge Elements: Quantitative and Computational Methods for Social Science. https://doi.org/10.1017/9781108684606.

Ceperley, DM, and Mark Dewing. 1999. “The Penalty Method for Random Walks with Uncertain Energies.” The Journal of Chemical Physics 110 (20): 9812–20.

Chan, Joshua, Gary Koop, Dale J Poirier, and Justin L Tobias. 2019. Bayesian Econometric Methods. Vol. 7. Cambridge University Press.

Chang, W. 2018. Web Application Framework for r: Package Shiny. R Studio. http://shiny.rstudio.com/.

Chen, Xiaohong, Timothy M. Christensen, and Elie Tamer. 2018. “Monte Carlo Confidence Sets for Identified Sets.” Econometrica 86 (6): 1965–2018. https://doi.org/10.3982/ECTA14525.

Chernozhukov, V., and H. Hong. 2003. “An MCMC Approach to Classical Estimation.” Journal of Econometrics 115: 293–346.

Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21: C1–68. https://doi.org/10.1111/ectj.12097.

Chernozhukov, Victor, and Christian Hansen. 2004. “The Effects of 401(k) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis.” The Review of Economics and Statistics 86 (3): 735–51. https://doi.org/10.1162/0034653041811749.

———. 2005. “An IV Model of Quantile Treatment Effects.” Econometrica 73 (1): 245–61. https://doi.org/10.1111/j.1468-0262.2005.00570.x.

Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. 2024. “Applied Causal Inference Powered by ML and AI.” arXiv preprint 2403.02467. arXiv.

Chernozhukov, Victor, and Han Hong. 2003. “An MCMC Approach to Classical Estimation.” Journal of Econometrics 115 (2): 293–346.

Chib, S. 1992. “Bayes Inference in the Tobit Censored Regression Model.” Journal of Econometrics 51: 79–99.

Chib, S., and B. Carlin. 1999. “On MCMC Sampling in Hierarchical Longitudinal Models.” Statistics and Computing 9: 17–26.

Chib, Siddhartha. 1993. “Bayes Regression with Autoregressive Errors: A Gibbs Sampling Approach.” Journal of Econometrics 58 (3): 275–94.

———. 1995. “Marginal Likelihood from the Gibbs Output.” Journal of the American Statistical Association 90 (432): 1313–21.

———. 2007. “Analysis of Treatment Response Data Without the Joint Distribution of Potential Outcomes.” Journal of Econometrics 140 (2): 401–12.

Chib, Siddhartha, and Edward Greenberg. 1994. “Bayes Inference in Regression Models with ARMA (p, q) Errors.” Journal of Econometrics 64 (1-2): 183–206.

———. 1995. “Understanding the Metropolis-Hastings Algorithm.” The American Statistician 49 (4): 327–35.

Chib, Siddhartha, Edward Greenberg, and Ivan Jeliazkov. 2009. “Estimation of Semiparametric Models in the Presence of Endogeneity and Sample Selection.” Journal of Computational and Graphical Statistics 18 (2): 321–48.

Chib, Siddhartha, Edward Greenberg, and Anna Simoni. 2023. “Nonparametric Bayes Analysis of the Sharp and Fuzzy Regression Discontinuity Designs.” Econometric Theory 39 (3): 481–533.

Chib, Siddhartha, and Liana Jacobi. 2016. “Bayesian Fuzzy Regression Discontinuity Analysis and Returns to Compulsory Schooling.” Journal of Applied Econometrics 31 (6): 1026–47.

Chib, Siddhartha, and Ivan Jeliazkov. 2001. “Marginal Likelihood from the Metropolis–Hastings Output.” Journal of the American Statistical Association 96 (453): 270–81.

Chib, Siddhartha, and Todd A Kuffner. 2016. “Bayes Factor Consistency.” arXiv Preprint arXiv:1607.00292.

Chib, Siddhartha, Minchul Shin, and Anna Simoni. 2018. “Bayesian Estimation and Comparison of Moment Condition Models.” Journal of the American Statistical Association 113 (524): 1656–68. https://doi.org/10.1080/01621459.2017.1358172.

Chipman, Hugh A, Edward I George, and Robert E McCulloch. 1998. “Bayesian CART Model Search.” Journal of the American Statistical Association 93 (443): 935–48. https://doi.org/10.1080/01621459.1998.10473750.

———. 2010. “BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98.

Choi, Taeryon, and Mark J. Schervish. 2007. “On Posterior Consistency in Nonparametric Regression Problems.” Journal of Multivariate Analysis 98 (10): 1969–87. https://doi.org/10.1016/j.jmva.2007.01.004.

Claeskens, Gerda, and Nils Lid Hjort. 2008. “Model Selection and Model Averaging.” Cambridge Books.

Clyde, M., and E. George. 2004. “Model Uncertatinty.” Statistical Science 19 (1): 81–94.

Conley, T., C. Hansen, and P. Rossi. 2012. “Plausibly Exogenous.” The Review of Economics and Statistics 94 (1): 260–72.

Conley, Timothy G, Christian B Hansen, Robert E McCulloch, and Peter E Rossi. 2008. “A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem.” Journal of Econometrics 144 (1): 276–305.

Cover, Thomas M., and Joy A. Thomas. 2006. Elements of Information Theory. 2nd ed. Hoboken, NJ: Wiley-Interscience.

Cragg, John G., and Stephen G. Donald. 1993. “Testing Identifiability and Specification in Instrumental Variable Models.” Econometric Theory 9 (2): 222–40. https://doi.org/10.1017/S0266466600007519.

Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven, CT: Yale University Press. https://mixtape.scunning.com.

Dahlin, Johan, and Thomas B Schön. 2019. “Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models.” Journal of Statistical Software 88: 1–41.

Dawid, A. P., M. Musio, and S. E. Fienberg. 2016. “From Statistical Evidence to Evidence of Causality.” Bayesian Analysis 11 (3): 725–52.

De Finetti, Bruno. 1937. “Foresight: Its Logical Laws, Its Subjective Sources.” In Studies in Subjective Probability, edited by H. E. Kyburg and H. E. Smokler. New York: Krieger.

De Jong, Piet, and Neil Shephard. 1995. “The Simulation Smoother for Time Series Models.” Biometrika 82 (2): 339–50.

Deaton, Angus. 2010. “Instruments, Randomization, and Learning about Development.” Journal of Economic Literature 48 (2): 424–55. https://doi.org/10.1257/jel.48.2.424.

DeGroot, M. H. 1975. Probability and Statistics. London: Addison-Wesley Publishing Co.

Del Moral, Pierre, Arnaud Doucet, and Ajay Jasra. 2012. “An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation.” Statistics and Computing 22: 1009–20.

Del Negro, Marco, and F. Schorfheide. 2011. “Forecasting with Bayesian VAR Models.” In The Oxford Handbook of Bayesian Econometrics, edited by John Geweke, Gary Koop, and Herman van Dijk, 224–54. Oxford University Press.

Diaconis, Persi, Donald Ylvisaker, et al. 1979. “Conjugate Priors for Exponential Families.” The Annals of Statistics 7 (2): 269–81.

Dickey, J. M., and E. Gunel. 1978. “Bayes Factors from Mixed Probabilities.” Journal of the Royal Statistical Society: Series B (Methodology) 40: 43–46.

Dickey, James M. 1971. “The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters.” The Annals of Mathematical Statistics, 204–23.

Diggle, Peter., P. Heagerty, Liang K-Y., and S. Zeger. 2002. Analysis of Longitudinal Data. Oxford University Press.

Ding, Peng. 2014. “Bayesian Robust Inference of Sample Selection Using Selection-t Models.” Journal of Multivariate Analysis 124: 451–64.

Doan, Thomas, Robert Litterman, and Christopher Sims. 1984. “Forecasting and Conditional Projection Using Realistic Prior Distributions.” Econometric Reviews 3 (1): 1–100.

Doucet, Arnaud, Nando De Freitas, and Neil Gordon. 2001. “An Introduction to Sequential Monte Carlo Methods.” Sequential Monte Carlo Methods in Practice, 3–14.

Doucet, Arnaud, Adam M Johansen, et al. 2009. “A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later.” Handbook of Nonlinear Filtering 12 (656-704): 3.

Drovandi, Christopher C, and Anthony N Pettitt. 2011a. “Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation.” Biometrics 67 (1): 225–33.

———. 2011b. “Likelihood-Free Bayesian Estimation of Multivariate Quantile Distributions.” Computational Statistics & Data Analysis 55 (9): 2541–56.

Drovandi, Christopher C, Anthony N Pettitt, and Anthony Lee. 2015. “Bayesian Indirect Inference Using a Parametric Auxiliary Model.” Statistical Science 30 (1): 72--95.

Drovandi, Christopher, and David T Frazier. 2022. “A Comparison of Likelihood-Free Methods with and Without Summary Statistics.” Statistics and Computing 32 (3): 42.

Duane, Simon, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth. 1987. “Hybrid Monte Carlo.” Physics Letters B 195 (2): 216–22.

Edwards, Y. D., and G. M. Allenby. 2003. “Multivariate Analysis of Multiple Response Data.” Journal of Marketing Research 40: 321–34.

Efron, B. 1979. “Bootstrap Methods: Another Look at the Jackknife.” The Annals of Statistics 7: 1–26.

Efron, Bradley, and Trevor Hastie. 2016. Computer Age Statistical Inference. Vol. 5. Cambridge University Press.

———. 2021. Computer Age Statistical Inference, Student Edition: Algorithms, Evidence, and Data Science. Vol. 6. Cambridge University Press.

Elhorst, J Paul et al. 2014. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Vol. 479. Springer.

Enders, Walter. 2014. Applied Econometric Time Series. 4th ed. Hoboken, NJ: Wiley.

Escobar, M., and M. West. 1995. “Bayesian Density Estimation and Inference Using Mixtures.” Journal of the American Statistical Association 90 (430): 577–88.

Ferguson, T. 1973. “A Bayesian Analysis of Some Nonparametric Problems.” The Annals of Statistics 1 (2): 209–30.

Fernandez, Carmen, Eduardo Ley, and Mark FJ Steel. 2001. “Benchmark Priors for Bayesian Model Averaging.” Journal of Econometrics 100 (2): 381–427.

Fisher, R. 1958. Statistical Methods for Research Workers. 13th ed. New York: Hafner.

Frazier, David T, and Christopher Drovandi. 2021. “Robust Approximate Bayesian Inference with Synthetic Likelihood.” Journal of Computational and Graphical Statistics 30 (4): 958–76.

Frazier, David T, Worapree Maneesoonthorn, Gael M Martin, and Brendan PM McCabe. 2019. “Approximate Bayesian Forecasting.” International Journal of Forecasting 35 (2): 521–39.

Frazier, David T, Gael M Martin, Christian P Robert, and Judith Rousseau. 2018. “Asymptotic Properties of Approximate Bayesian Computation.” Biometrika 105 (3): 593–607.

Frazier, David T, Christian P Robert, and Judith Rousseau. 2020. “Model Misspecification in Approximate Bayesian Computation: Consequences and Diagnostics.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (2): 421–44.

Frazier, David, David J Nott, Christopher Drovandi, and Robert Kohn. 2023. “Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments.” Journal of the American Statistical Association 118 (544): 2821--2832.

Freund, Yoav, and Robert E Schapire. 1997. “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences 55 (1): 119–39.

Friedman, Jerome H. 1991. “Multivariate Adaptive Regression Splines.” Annals of Statistics 19 (1): 1–67. https://doi.org/10.1214/aos/1176347963.

———. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232.

Frühwirth-Schnatter, Sylvia. 1994. “Data Augmentation and Dynamic Linear Models.” Journal of Time Series Analysis 15 (2): 183–202.

———. 2006. Finite Mixture and Markov Switching Models. Springer.

Fu, Wenjiang J. 1998. “Penalized Regression: The Bridge Versus the Lasso.” Journal of Computational and Graphical Statistics 7 (3): 397–416. https://doi.org/10.1080/10618600.1998.10474784.

Furnival, George M., and R. W. Wilson. 1974a. “Regressions by Leaps and Bounds.” Technometrics 16 (4): 499–511. https://doi.org/10.1080/00401706.1974.10489152.

Furnival, George M., and Robert W. Wilson. 1974b. “Regressions by Leaps and Bounds.” Technometrics 16 (4): 499–511.

Gardner, Jacob R., Geoff Pleiss, David Bindel Wu, Kilian Q. Weinberger, and Andrew Gordon Wilson. 2018. “Product Kernel Interpolation for Scalable Gaussian Processes.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84:1407–16. PMLR.

Garthwaite, P., J. Kadane, and A. O’Hagan. 2005. “Statistical Methods for Eliciting Probability Distributions.” Journal of American Statistical Association 100 (470): 680–701.

Gelfand, A. E., and A. F. M. Smith. 1990. “Sampling-Based Approaches to Calculating Marginal Densities.” Journal of the American Statistical Association 85: 398–409.

Gelfand, Alan E, and Dipak K Dey. 1994. “Bayesian Model Choice: Asymptotics and Exact Calculations.” Journal of the Royal Statistical Society: Series B (Methodological) 56 (3): 501–14.

Gelman, A., and X. Meng. 1996. “Model Checking and Model Improvement.” In In Markov Chain Monte Carlo in Practice, edited by Gilks, Richardson, and Speigelhalter. Springer US.

Gelman, A., X. Meng, and H. Stern. 1996. “Posterior Predictive Assessment of Model Fitness via Realized Discrepancies.” Statistica Sinica, 733–60.

Gelman, Andrew et al. 2006. “Prior Distributions for Variance Parameters in Hierarchical Models (Comment on Article by Browne and Draper).” Bayesian Analysis 1 (3): 515–34.

Gelman, Andrew, John B Carlin, Hal S Stern, David Dunson, Aki Vehtari, and Donald B Rubin. 2021. Bayesian Data Analysis. Chapman; Hall/CRC.

Gelman, Andrew, Walter R Gilks, and Gareth O Roberts. 1997. “Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms.” The Annals of Applied Probability 7 (1): 110–20.

Gelman, Andrew, and Guido Imbens. 2013. “Why Ask Why? Forward Causal Inference and Reverse Causal Questions.” National Bureau of Economic Research.

Gelman, Andrew, and Donald B. Rubin. 1992. “Inference from Iterative Simulation Using Multiple Sequences.” Statistical Science 7 (4): 457–72. https://doi.org/10.1214/ss/1177011136.

Geman, S, and D. Geman. 1984. “Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–41.

George, Edward I, and Robert E McCulloch. 1993. “Variable Selection via Gibbs Sampling.” Journal of the American Statistical Association 88 (423): 881–89.

———. 1997. “Approaches for Bayesian Variable Selection.” Statistica Sinica 7: 339–73.

George, E., and R. McCulloch. 1993. “Variable Selection via Gibbs Sampling.” Journal of the American Statistical Association 88 (423): 881–89.

———. 1997. “Approaches for Bayesian Variable Selection.” Statistica Sinica 7: 339–73.

Geweke, J. 1992. “Bayesian Statistics.” In. Clarendon Press, Oxford, UK.

Geweke, John. 1999. “Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication.” Econometric Reviews 18 (1): 1–73.

———. 2004. “Getting It Right: Joint Distribution Tests of Posterior Simulators.” Journal of the American Statistical Association 99 (467): 799–804.

———. 2005. Contemporary Bayesian Econometrics and Statistics. Vol. 537. John Wiley & Sons.

Geweke, John, Gary Koop, and Herman K van Dijk. 2011. The Oxford Handbook of Bayesian Econometrics. Oxford University Press, USA.

Geyer, Charles J. 1992. “Practical Markov Chain Monte Carlo.” Statistical Science, 473–83.

Giacomini, Raffaella, and Tetsuya Kitagawa. 2021. “Robust Bayesian Inference for Set-Identified Models.” Econometrica 89 (4): 1519–56. https://doi.org/10.3982/ECTA17276.

Gill, Richard D, and James M Robins. 2001. “Causal Inference for Complex Longitudinal Data: The Continuous Case.” Annals of Statistics, 1785–1811.

Giordano, Ryan, Runjing Liu, Michael I Jordan, and Tamara Broderick. 2022. “Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics.” Bayesian Analysis 1 (1): 1–34.

Good, I. J. 1992. “The Bayes/Non Bayes Compromise: A Brief Review.” Journal of the American Statistical Association 87 (419): 597–606.

Goodman, S. N. 1999. “Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy.” Annals of Internal Medicine 130 (12): 995–1004.

Gordon, N. J., D. J. Salmond, and A. F. Smith. 1993. “Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation.” IEE Proceedings F (Radar and Signal Processing) 140 (2): 107–13.

Green, P. J. 1995. “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination.” Biometrika 82: 711–32.

Greenberg, David S, Marcel Nonnenmacher, and Jakob H Macke. 2019. “Automatic Posterior Transformation for Likelihood-Free Inference.” In International Conference on Machine Learning, 2404–14.

Greenberg, Edward. 2012. Introduction to Bayesian Econometrics. Cambridge University Press.

Gujarati, Damodar N., and Dawn C. Porter. 2009. Basic Econometrics. 5th ed. New York, NY: McGraw-Hill Education.

Gustafson, Paul. 2000. “Local Robustness in Bayesian Analysis.” In Robust Bayesian Analysis, 71–88. Springer.

Haavelmo, Trygve. 1943. “The Statistical Implications of a System of Simultaneous Equations.” Econometrica 11 (1): 1–12. https://doi.org/10.2307/1905714.

Hahn, Jinyong, Petra Todd, and Wilbert van der Klaauw. 2001. “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design.” Econometrica 69 (1): 201–9. https://doi.org/10.1111/1468-0262.00183.

Hahn, P Richard, Jared S Murray, and Carlos M Carvalho. 2020. “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion).” Bayesian Analysis 15 (3): 965–1056.

Haining, Robert. 1990. Spatial Data Analysis in the Social and Environmental Sciences. First. United of Kingdom: Cambridge University Press.

Handschin, Johannes Edmund, and David Q Mayne. 1969. “Monte Carlo Techniques to Estimate the Conditional Expectation in Multi-Stage Non-Linear Filtering.” International Journal of Control 9 (5): 547–59.

Hansen, Lars Peter. 1982. “Large Sample Properties of Generalized Method of Moments Estimators.” Econometrica 50 (4): 1029–54. https://doi.org/10.2307/1912775.

Hastie, Trevor, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani. 2022. “Surprises in High-Dimensional Ridgeless Least Squares Interpolation.” Annals of Statistics 50 (2): 949.

Hastie, Trevor, and Robert Tibshirani. 2000. “Bayesian Backfitting (with Discussion).” Journal of the American Statistical Association 95 (452): 1228–40. https://doi.org/10.1080/01621459.2000.10474339.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer. https://link.springer.com/book/10.1007/978-0-387-84858-7.

Hastings, W. 1970. “Monte Carlo Sampling Methods Using Markov Chains and Their Application.” Biometrika 57: 97–109.

Hayashi, Fumio. 2000. Econometrics. Princeton University Press.

Heckman, James J. 1979. “Sample Selection Bias as a Specification Error.” Econometrica 47 (1): 153–61. https://doi.org/10.2307/1912352.

———. 1990. “Varieties of Selection Bias.” American Economic Review 80 (2): 313–18. http://www.jstor.org/stable/2006539.

Heckman, James J., Hedibert F. Lopes, and Rémi Piatek. 2014. “Treatment Effects: A Bayesian Perspective.” Econometric Reviews 33 (1-4): 36–67. https://doi.org/10.1080/07474938.2013.807103.

Heckman, James J., and Edward Vytlacil. 2005. “Structural Equations, Treatment Effects, and Econometric Policy Evaluation.” Econometrica 73 (3): 669–738. https://doi.org/10.1111/j.1468-0262.2005.00594.x.

Heidelberger, P., and P. D. Welch. 1983. “Simulation Run Length Control in the Presence of an Initial Transient.” Operations Research 31 (6): 1109–44.

Helmut, Lütkepohl. 2005. New Introduction to Multiple Time Series Analysis. Springer.

Hernán, Miguel A., and James M. Robins. 2020. Causal Inference: What If. Boca Raton, FL: Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/.

Hill, Jennifer L. 2011. “Bayesian Nonparametric Modeling for Causal Inference.” Journal of Computational and Graphical Statistics 20 (1): 217–40.

Hirano, Keisuke, Guido W. Imbens, Donald B. Rubin, and Xiaohong Zhou. 2000. “Assessing the Effect of an Influenza Vaccine in an Encouragement Design.” Biostatistics 1 (1): 69–88. https://doi.org/10.1093/biostatistics/1.1.69.

Hoerl, Arthur E., and Robert W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1): 55–67. https://doi.org/10.1080/00401706.1970.10488634.

Hoeting, J., D. Madigan, A. Raftery, and C. Volinsky. 1999. “Bayesian Model Averaging: A Tutorial.” Statistical Science 14 (4): 382–417.

Hoffman, Matthew D, David M Blei, Chong Wang, and John Paisley. 2013. “Stochastic Variational Inference.” The Journal of Machine Learning Research 14 (1): 1303–47.

Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60. https://doi.org/10.2307/2289064.

Hosszejni, Daniel, and Gregor Kastner. 2021. “Modeling Univariate and Multivariate Stochastic Volatility in r with stochvol and factorstochvol.” Journal of Statistical Software 100 (12): 1–34. https://doi.org/10.18637/jss.v100.i12.

Huang, Zhen, and Andrew Gelman. 2005. “Sampling for Bayesian Computation with Large Datasets.” Technical Report. Department of Statistics, Columbia University.

Ibrahim, Joseph G., and Purushottam W. Laud. 1991. “On Bayesian Analysis of Generalized Linear Models Using Jeffreys’s Prior.” Journal of the American Statistical Association 86 (416): 981–86.

Imbens, Guido W. 2014. “Instrumental Variables: An Econometrician’s Perspective.” Statistical Science 29 (3): 323–58. https://doi.org/10.1214/14-STS480.

Imbens, Guido W., and Joshua D. Angrist. 1994. “Identification and Estimation of Local Average Treatment Effects.” Econometrica 62 (2): 467–75. https://doi.org/10.2307/2951620.

Imbens, Guido W., and Thomas Lemieux. 2008. “Regression Discontinuity Designs: A Guide to Practice.” Journal of Econometrics 142 (2): 615–35. https://doi.org/10.1016/j.jeconom.2007.05.001.

Imbens, Guido W., and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. New York, NY: Cambridge University Press. https://doi.org/10.1017/CBO9781139025751.

Imbens, Guido W, and Donald B Rubin. 1997. “Bayesian Inference for Causal Effects in Randomized Experiments with Noncompliance.” The Annals of Statistics, 305–27.

Ishwaran, H., and J. S. Rao. 2005. “Spike and Slab Variable Selection: Frequentist and Bayesian Strategies.” The Annals of Statistics 33 (2): 730–73.

Jacobi, Liana, Chun Fung Kwok, Andrés Ramı́rez-Hassan, and Nhung Nghiem. 2024. “Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference.” Studies in Nonlinear Dynamics & Econometrics 28 (2): 403–34.

Jacobi, Liana, Dan Zhu, and Mark Joshi. 2022. “Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions.” Available at SSRN 3347399. https://ssrn.com/abstract=3347399 or http://dx.doi.org/10.2139/ssrn.3347399.

Jeffreys, H. 1935. “Some Test of Significance, Treated by the Theory of Probability.” Proccedings of the Cambridge Philosophy Society 31: 203–22.

———. 1961. Theory of Probability. London: Oxford University Press.

Jeffreys, Harold. 1946. “An Invariant Form for the Prior Probability in Estimation Problems.” Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 186 (1007): 453–61.

Jetter, Michael, Rafat Mahmood, Christopher F. Parmeter, and Andrés Ramı́rez-Hassan. 2022. “Post-Cold War Civil Conflict and the Role of History and Religion: A Stochastic Search Variable Selection Approach.” Economic Modelling 114: 105907. https://doi.org/10.1016/j.econmod.2022.105907.

Jetter, M., and A. Ramírez Hassan. 2015. “Want Export Diversification? Educate the Kids First.” Economic Inquiry 53 (4): 1765–82.

Jhonson, V. E., and D. Rossell. 2012. “Bayesian Model Selection in High-Dimensional Settings.” Journal of the American Statistical Association 107 (498): 649–60.

Jiang, Bai. 2018. “Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy.” In International Conference on Artificial Intelligence and Statistics, 1711–21. PMLR.

Jiang, Wenhua, and Martin A. Tanner. 2008. “Gibbs Posterior for Variable Selection in High-Dimensional Classification and Data Mining.” Annals of Statistics 36 (5): 2207–31. https://doi.org/10.1214/07-AOS547.

Johnson, Valen E, and David Rossell. 2012. “Bayesian Model Selection in High-Dimensional Settings.” Journal of the American Statistical Association 107 (498): 649–60.

Jordan, Michael I, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. “An Introduction to Variational Methods for Graphical Models.” Machine Learning 37: 183–233.

Kadane, J. B. 1980. “Predictive and Structural Methods for Eliciting Prior Distributions.” In Bayesian Analysis in Econometrics and Statistics: Essays in Honor of Harold Jeffreys, edited by A Zellner, 89–93. Amsterdam: North–Holland Publishing Company,.

Kadane, Joseph, and Lara Wolfson. 1998. “Experiences in Elitation.” The Statiscian 47 (1): 3–19.

Kahneman, Daniel. 2011. Thinking, Fast and Slow. Macmillan.

Kantas, N., A. Doucet, S. S. Singh, and J. M. Maciejowski. 2009. “An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State–Space Models.” IFAC Proceedings Volumes 42 (10): 774–85.

Kantas, Nikolas, Arnaud Doucet, Sumeetpal S Singh, Jan Maciejowski, Nicolas Chopin, et al. 2015. “On Particle Methods for Parameter Estimation in State-Space Models.” Statistical Science 30 (3): 328–51.

Karabatsos, G. 2016. “A Menu-Driven Software Package of Bayesian Nonparametric (and Parametric) Mixed Models for Regression Analysis and Density Estimation.” Behavior Research Methods 49: 335–62.

Karl, A., and A. Lenkoski. 2012. “Instrumental Variable Bayesian Model Averaging via Conditional Bayes Factor.” Heidelberg University.

Kass, R E, and A E Raftery. 1995. “Bayes factors.” Journal of the American Statistical Association 90 (430): 773–95.

Kass, R. 2011. “Statistical Inference: The Big Picture.” Statistical Science 26 (1): 1–9.

Kastner, Gregor, and Sylvia Frühwirth-Schnatter. 2014. “Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models.” Computational Statistics & Data Analysis 76: 408–23.

Kim, Chang-Jin, and Charles R Nelson. 1999. “Has the US Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle.” Review of Economics and Statistics 81 (4): 608–16.

Kleibergen, Frank, and Richard Paap. 2006. “Generalized Reduced Rank Tests Using the Singular Value Decomposition.” Journal of Econometrics 133 (1): 97–126. https://doi.org/10.1016/j.jeconom.2005.02.011.

Kleijn, Bas J. K., and Aad W. van der Vaart. 2006. “Misspecification in Infinite-Dimensional Bayesian Statistics.” Annals of Statistics 34 (2): 837–77. https://doi.org/10.1214/009053606000000029.

Kong, Augustine, Jun S Liu, and Wing Hung Wong. 1994. “Sequential Imputations and Bayesian Missing Data Problems.” Journal of the American Statistical Association 89 (425): 278–88.

Koop, Gary M. 2003. Bayesian Econometrics. John Wiley & Sons Inc.

Koop, Gary, Dimitris Korobilis, et al. 2010. “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics.” Foundations and Trends in Econometrics 3 (4): 267–358.

Koop, Gary, and Dale J Poirier. 1997. “Learning about the Across-Regime Correlation in Switching Regression Models.” Journal of Econometrics 78 (2): 217–27.

Koop, G, R León-Gonzalez, and R Strachan. 2012. “Bayesian Model Averaging in the Instrumental Variable Regression Model.” Journal of Econometrics 171: 237–50.

Kozumi, Hideo, and Genya Kobayashi. 2011. “Gibbs Sampling Methods for Bayesian Quantile Regression.” Journal of Statistical Computation and Simulation 81 (11): 1565--1578.

Krueger, Fabian. 2022. Bvarsv: Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters. https://doi.org/10.32614/CRAN.package.bvarsv.

Kucukelbir, Alp, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M Blei. 2017. “Automatic Differentiation Variational Inference.” Journal of Machine Learning Research 18 (14): 1–45.

Kyung, Minjung, Jeff Gill, Malay Ghosh, and George Casella. 2010. “Penalized Regression, Standard Errors, and Bayesian Lassos.” Bayesian Analysis 5 (2): 369–411. https://doi.org/10.1214/10-BA607.

Lancaster, Tony. 2004. An Introduction to Modern Bayesian Econometrics. Blackwell Oxford.

Laplace, P. 1812. Théorie Analytique Des Probabilités. Courcier.

Laplace, Pierre Simon. 1774. “Mémoire Sur La Probabilité de Causes Par Les évenements.” Mémoire de l’académie Royale Des Sciences.

Lazar, Nicole A. 2021. “A Review of Empirical Likelihood.” Annual Review of Statistics and Its Application 8 (1): 329–44.

Lazar, Nicole A. 2003. “Bayesian Empirical Likelihood.” Biometrika 90 (2): 319–26. https://doi.org/10.1093/biomet/90.2.319.

Lehmann, E. L., and George Casella. 2003. Theory of Point Estimation. Second Edition. Springer.

Lenkoski, Alex, Theo S. Eicher, and Adrian Raftery. 2014. “Two-Stage Bayesian Model Averaging in Endogeneous Variable Models.” Econometric Reviews 33.

Lenkoski, Alex, Anna Karl, and Andreas Neudecker. 2013. Package ivbma. https://CRAN.R-project.org/package=ivbma.

Lenormand, Maxime, Franck Jabot, and Guillaume Deffuant. 2013. “Adaptive Approximate Bayesian Computation for Complex Models.” Computational Statistics 28 (6): 2777–96.

LeSage, James, and Robert Kelley Pace. 2009. Introduction to Spatial Econometrics. Chapman; Hall/CRC.

Leuenberger, Christoph, and Daniel Wegmann. 2010. “Bayesian Computation and Model Selection Without Likelihoods.” Genetics 184 (1): 243–52.

Lewbel, Arthur, and Krishna Pendakur. 2009. “Tricks with Hicks: The EASI Demand System.” American Economic Review 99 (3): 827–63.

Ley, Eduardo, and Mark FJ Steel. 2009. “On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression.” Journal of Applied Econometrics 24 (4): 651–74.

Li, Wei, Sangwoo Ahn, and Max Welling. 2016. “Scalable MCMC for Mixed Membership Stochastic Blockmodels.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), edited by JMLR Workshop and Conference Proceedings, 51:723–31.

Lindley, D. V. 2000. “The Philosophy of Statistics.” The Statistician 49 (3): 293–337.

Lindley, D. V., and L. D. Phillips. 1976. “Inference for a Bernoulli Process (a Bayesian View).” American Statistician 30: 112–19.

Lindley, Dennis V. 1957. “A Statistical Paradox.” Biometrika 44 (1/2): 187–92.

Linero, Antonio R. 2018. “Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection.” Journal of the American Statistical Association 113 (522): 626–36.

Lintusaari, Jarno, Michael U Gutmann, Ritabrata Dutta, Samuel Kaski, and Jukka Corander. 2017. “Fundamentals and Recent Developments in Approximate Bayesian Computation.” Systematic Biology 66 (1): e66–82.

Litterman, Robert B. 1986. “Forecasting with Bayesian Vector Autoregressions—Five Years of Experience.” Journal of Business & Economic Statistics 4 (1): 25–38.

Little, Roderick J. A., and Donald B. Rubin. 2019. Statistical Analysis with Missing Data. 3rd ed. Wiley Series in Probability and Statistics. John Wiley & Sons.

Liu, J. S., and R. Chen. 1995. “Blind Deconvolution via Sequential Imputations.” Journal of the American Statistical Association 90 (430): 567–76.

Liu, Pangpang, and Yichuan Zhao. 2023. “A Review of Recent Advances in Empirical Likelihood.” Wiley Interdisciplinary Reviews: Computational Statistics 15 (3): e1599.

Lo, Albert Y. 1987. “A Large Sample Study of the Bayesian Bootstrap.” The Annals of Statistics 15 (1): 360–75. https://doi.org/10.1214/aos/1176350271.

Lueckmann, Jan-Matthis, Pedro J Goncalves, Gabriele Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H Macke. 2017. “Flexible Statistical Inference for Mechanistic Models of Neural Dynamics.” Advances in Neural Information Processing Systems 30.

Luis, Gruber, and Kastner Gregor. 2024. BayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions. https://doi.org/10.32614/CRAN.package.bayesianVARs.

Luo, Yu, Daniel J. Graham, and Emma J. McCoy. 2023. “Semiparametric Bayesian Doubly Robust Causal Estimation.” Journal of Statistical Planning and Inference 225: 171–87. https://doi.org/10.1016/j.jspi.2022.12.005.

Maclaurin, Dougal, and Ryan P. Adams. 2015. “Firefly Monte Carlo: Exact MCMC with Subsets of Data.” In The Twenty-Fourth International Joint Conference on Artificial Intelligence, 4279–95. IJCAI.

Madigan, David, Jeremy York, and Denis Allard. 1995. “Bayesian Graphical Models for Discrete Data.” International Statistical Review 63: 215–32.

Madigan, D., and A. E. Raftery. 1994. “Model selection and accounting for model uncertainty in graphical models using Occam’s window.” Journal of the American Statistical Association 89 (428): 1535–46.

Madigan, D., J. C. York, and D. Allard. 1995. “Bayesian Graphical Models for Discrete Data.” International Statistical Review 63 (2): 215–32.

Manski, Charles F. 1989. “Anatomy of the Selection Problem.” The Journal of Human Resources 24 (3): 343–60. https://doi.org/10.2307/145829.

———. 1990. “Nonparametric Bounds on Treatment Effects.” American Economic Review 80 (2): 319–23. https://www.jstor.org/stable/2006592.

———. 1995. Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.

———. 2003. Partial Identification of Probability Distributions. New York: Springer. https://doi.org/10.1007/b97446.

Marjoram, Paul, John Molitor, Vincent Plagnol, and Simon Tavaré. 2003. “Markov Chain Monte Carlo Without Likelihoods.” Proceedings of the National Academy of Sciences of the USA 100 (26): 15324–28.

Marschak, Jacob, and William H. Andrews. 1944. “Random Simultaneous Equations and the Theory of Production.” Econometrica 12 (3-4): 143–205. https://doi.org/10.2307/1905494.

Martin, Andrew D., Kevin M. Quinn, and Jong Hee Park. 2011. “MCMCpack: Markov Chain Monte Carlo in R.” Journal of Statistical Software 42 (9): 1–21.

———. 2018. Package MCMCpack.

Martin, Gael M, David T Frazier, and Christian P Robert. 2024a. “Approximating Bayes in the 21st Century.” Statistical Science 39 (1): 20–45.

———. 2024b. “Computing Bayes: From Then ‘Til Now.” Statistical Science 39 (1): 3–19.

Martin, Gael M, Rubén Loaiza-Maya, Worapree Maneesoonthorn, David T Frazier, and Andrés Ramı́rez-Hassan. 2022. “Optimal Probabilistic Forecasts: When Do They Work?” International Journal of Forecasting 38 (1): 384–406.

Martin, Osvaldo A., Ravin Kumar, and Junpeng Lao. 2021. Bayesian Modeling and Computation in Python. Boca Raton: publisher=Chapman and Hall/CRC.

Martino, Sara, and Andrea Riebler. 2019. “Integrated Nested Laplace Approximations (INLA).” arXiv Preprint arXiv:1907.01248.

McCormick, Tyler H., Adrian Raftery, David Madigan, Sevvandi Kandanaarachchi, and Hana Sevcikova. 2018. Dma: Dynamic Model Averaging. https://doi.org/10.32614/CRAN.package.dma.

McCormick, Tyler H, Adrian E Raftery, David Madigan, and Randall S Burd. 2012. “Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification.” Biometrics 68 (1): 23–30.

McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models. London: Chapman; Hall.

McCulloch, Robert E, Nicholas G Polson, and Peter E Rossi. 2000. “A Bayesian Analysis of the Multinomial Probit Model with Fully Identified Parameters.” Journal of Econometrics 99 (1): 173–93.

McCulloch, R., and P. Rossi. 1994. “An Exact Likelihood Analysis of the Multinomial Probit Model.” Journal of Econometrics 64: 207–40.

McGrayne, Sharon Bertsch. 2011. The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted down Russian Submarines, & Emerged Triumphant from Two Centuries of c. Yale University Press.

Mertens, Karel, and Morten O Ravn. 2014. “A Reconciliation of SVAR and Narrative Estimates of Tax Multipliers.” Journal of Monetary Economics 68: S1–19.

Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. 1953. “Equations of State Calculations by Fast Computing Machines.” J. Chem. Phys 21: 1087–92.

Miller, J., and M. Harrison. 2014. “Inconsistency of Pitman-Yor Process Mixtures for the Number of Components.” Journal of Machine Learning Research 15: 3333–70.

Minsker, Stanislav. 2015. “Scalable and Robust Bayesian Inference via the Median Posterior.” Edited by Francis Bach and David Blei. Proceedings of the 31st International Conference on Machine Learning (ICML) 37: 1656–64.

Minsker, Stanislav, Subhabrata Srivastava, Lin Lin, and David B. Dunson. 2017. “Robust and Scalable Bayes via a Median of Subset Posterior Measures.” Journal of Machine Learning Research 18 (1): 4488–4527.

Mohr, Franz X. 2024. bvartools: Bayesian Inference of Vector Autoregressive and Error Correction Models. https://CRAN.R-project.org/package=bvartools.

Moon, Hyungsik Roger, and Frank Schorfheide. 2012. “Bayesian and Frequentist Inference in Partially Identified Models.” Econometrica 80 (2): 755–82. https://doi.org/10.3982/ECTA8969.

Morgan, John P, N Rao Chaganty, Ram C Dahiya, and Michael J Doviak. 1991. “Let’s Make a Deal: The Player’s Dilemma.” The American Statistician 45 (4): 284–87.

Müller, P., F. Quintana, A. Jara, and T. Hanson. 2015. Bayesian Nonparametric Data Analysis. New York: Springer.

Müller, Ulrich K. 2013. “Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix.” Econometrica 81 (5): 1805–49. https://doi.org/10.3982/ECTA9097.

Neal, Radford M. 2012. Bayesian Learning for Neural Networks. Vol. 118. Springer Science & Business Media.

Neal, Radford M. 1996. Bayesian Learning for Neural Networks. Vol. 118. Lecture Notes in Statistics. Springer. https://doi.org/10.1007/978-1-4612-0745-0.

———. 2011. “MCMC Using Hamiltonian Dynamics.” In Handbook of Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, 113–62. Chapman; Hall/CRC.

Neiswanger, Willie, Chong Wang, and Eric P Xing. 2013. “Asymptotically Exact, Embarrassingly Parallel MCMC.” In Proceedings of the Thirtieth International Conference on Machine Learning, 623–32. PMLR.

Nelder, J. A., and R. W. M. Wedderburn. 1972. “Generalized Linear Models.” Journal of the Royal Statistical Society: Series A (General) 135 (3): 370–84.

Nemeth, Christopher, and Paul Fearnhead. 2021. “Stochastic Gradient Markov Chain Monte Carlo.” Journal of the American Statistical Association 116 (533): 433–50.

Neyman, J., and E. Pearson. 1933. “On the Problem of the Most Efficient Tests of Statistical Hypotheses.” Philosophical Transactions of the Royal Society, Series A 231: 289–337.

Nguyen, Duy. 2023. “An in Depth Introduction to Variational Bayes Note.” Available at SSRN 4541076.

Nikolas, Kuschnig, Vashold Lukas, Tomass Nirai, McCracken Michael, and Ng Serena. 2022. BVAR: Hierarchical Bayesian Vector Autoregression. https://doi.org/10.32614/CRAN.package.BVAR.

Nobile, Agostino. 2000. “Comment: Bayesian Multinomial Probit Models with a Normalization Constraint.” Journal of Econometrics 99 (2): 335–45.

Normington, James P, Eric F Lock, Thomas A Murray, and Caroline S Carlin. 2022. “Bayesian Variable Selection in Hierarchical Difference-in-Differences Models.” Statistical Methods in Medical Research 31 (1): 169–83.

O’Hara, Robert B., and Mikko J. Sillanpää. 2009. “A Review of Bayesian Variable Selection Methods: What, How and Which.” Bayesian Analysis 4 (1): 85–118. https://doi.org/10.1214/09-BA403.

Ord, K. 1975. “Estimation Methods for Models of Spatial Interaction.” Journal of the American Statistical Association 70 (349): 120–26.

Papamakarios, George, and Iain Murray. 2016. “Fast \(\epsilon\)-Free Inference of Simulation Models with Bayesian Conditional Density Estimation.” In Advances in Neural Information Processing Systems. Vol. 29.

Park, T., and G. Casella. 2008. “The Bayesian Lasso.” Journal of the American Statistical Association 103 (482): 681–86.

Parmigiani, G., and L. Inoue. 2008. Decision Theory Principles and Approaches. John Wiley & Sons.

Pearl, Judea. 1995. “Causal Diagrams for Empirical Research.” Biometrika 82 (4): 669–710. https://doi.org/10.1093/biomet/82.4.669.

Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.

Pericchi, Luis, and Carlos Pereira. 2015. “Adaptative significance levels using optimal decision rules: Balancing by weighting the error probabilities.” Brazilian Journal of Probability and Statistics.

Petris, Giovanni. 2010. “An R Package for Dynamic Linear Models.” Journal of Statistical Software 36 (12): 1–16. https://www.jstatsoft.org/v36/i12/.

Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli. 2009. “Dynamic Linear Models.” In Dynamic Linear Models with r, 31–84. Springer.

Pitt, Michael K, Ralph dos Santos Silva, Paolo Giordani, and Robert Kohn. 2012. “On Some Properties of Markov Chain Monte Carlo Simulation Methods Based on the Particle Filter.” Journal of Econometrics 171 (2): 134–51.

Pleiss, Geoff, Jacob R. Gardner, Kilian Q. Weinberger, and Andrew Gordon Wilson. 2018. “Constant-Time Predictive Distributions for Gaussian Processes.” In Proceedings of the 35th International Conference on Machine Learning (ICML), 80:4111–20. PMLR.

Plummer, Martyn, Nicky Best, Kate Cowles, Karen Vines, Deepayan Sarkar, Douglas Bates, Russell Almond, and Arni Magnusson. 2016. Output Analysis and Diagnostics for MCMC. https://CRAN.R-project.org/package=coda.

Pole, Andy, Mike West, and Jeff Harrison. 2018. Applied Bayesian Forecasting and Time Series Analysis. Chapman; Hall/CRC.

Price, Leah F, Christopher C Drovandi, Anthony Lee, and David J Nott. 2018. “Bayesian Synthetic Likelihood.” Journal of Computational and Graphical Statistics 27 (1): 1–11.

Pritchard, Jonathan K, Mark T Seielstad, Anna Perez-Lezaun, and Marcus W Feldman. 1999. “Population Growth of Human Y Chromosomes: A Study of Y Chromosome Microsatellites.” Molecular Biology and Evolution 16 (12): 1791–98.

Quiroz, Matias, Robert Kohn, Mattias Villani, and Minh-Ngoc Tran. 2019. “Speeding up MCMC by Efficient Data Subsampling.” Journal of the American Statistical Association.

Quiroz, Matias, Mattias Villani, Robert Kohn, Minh-Ngoc Tran, and Khue-Dung Dang. 2018. “Subsampling MCMC—a Review for the Survey Statistician.” arXiv Preprint arXiv:1807.08409.

R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.

———. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Raftery, A. 1995. “Bayesian Model Selection in Social Research.” Sociological Methodology 25: 111–63.

Raftery, A. E., and S. M. Lewis. 1992. “One Long Run with Diagnostics: Implementation Strategies for Markov Chain Monte Carlo.” Statistical Science 7: 493–97.

Raftery, Adrian E. 1993. “Bayesian Model Selection in Structural Equation Models.” Sage Focus Editions 154: 163–63.

Raftery, Adrian E, Miroslav Kárnỳ, and Pavel Ettler. 2010. “Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill.” Technometrics 52 (1): 52–66.

Raftery, Adrian E., David Madigan, and Jennifer A. Hoeting. 1997. “Bayesian Model Averaging for Linear Regression Models.” Journal of the American Statistical Association 92 (437): 179–91.

Raftery, Adrian, Jennifer Hoeting, Chris Volinsky, Ian Painter, and Ka Yee Yeung. 2012. Package BMA. https://CRAN.R-project.org/package=BMA.

Ramírez Hassan, A. 2017. “The Interplay Between the Bayesian and Frequentist Approaches: A General Nesting Spatial Panel Data Model.” Spatial Economic Analysis 12 (1): 92–112.

Ramírez Hassan, A., J. Cardona Jiménez, and R. Cadavid Montoya. 2013. “The Impact of Subsidized Health Insurance on the Poor in Colombia: Evaluating the Case of Medellín.” Economia Aplicada 17 (4): 543–56.

Ramírez-Hassan, A., and M. Graciano-Londoño. 2020. “A GUIded Tour of Bayesian Regression.” The R Journal 13 (2): 135–52.

Ramírez-Hassan, A., and R. Guerra-Urzola. 2021. “Bayesian Treatment Effects Due to a Subsidized Health Program: The Case of Preventive Health Care Utilization in Medellín (Colombia).” Empirical Economics 60: 1477–1506.

Ramı́rez Hassan, Andrés, and Santiago Montoya Blandón. 2019. “Welfare Gains of the Poor: An Endogenous Bayesian Approach with Spatial Random Effects.” Econometric Reviews 38 (3): 301–18.

Ramı́rez-Hassan, Andrés. 2020. “Dynamic Variable Selection in Dynamic Logistic Regression: An Application to Internet Subscription.” Empirical Economics 59 (2): 909–32.

Ramı́rez-Hassan, Andrés, and Daniela A. Carvajal-Rendón. 2021. “Specification Uncertainty in Modeling Internet Adoption: A Developing City Case Analysis.” Utilities Policy 70: 101218.

Ramı́rez-Hassan, Andrés, and David T. Frazier. 2024. “Testing Model Specification in Approximate Bayesian Computation Using Asymptotic Properties.” Journal of Computational and Graphical Statistics 33 (3): 1–14.

Ramı́rez-Hassan, Andrés, Gustavo A. Garcı́a, Estefanı́a Saravia, Juan Fernando Duque, and Daniel Londoño. 2023. “What Kind of Schools Parents Choose When They Have More Options? Effects of School Transport Subsidies.” Socio-Economic Planning Sciences 87: 101509. https://doi.org/10.1016/j.seps.2023.101509.

Ramı́rez–Hassan, Andrés, and Alejandro López-Vera. 2024. “Welfare Implications of a Tax on Electricity: A Semi-Parametric Specification of the Incomplete EASI Demand System.” Energy Economics 131: 1–13.

Ramsey, F. 1926. “Truth and Probability.” In The Foundations of Mathematics and Other Logical Essays, edited by Routledge and Kegan Paul. London: New York: Harcourt, Brace; Company.

Rasmussen, Carl Edward, and Christopher K. I. Williams. 2006. Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press.

Rayner, Glen D, and Helen L MacGillivray. 2002. “Numerical Maximum Likelihood Estimation for the g-and-k and Generalized g-and-h Distributions.” Statistics and Computing 12 (1): 57–75.

Rendell, Lewis J, Adam M Johansen, Anthony Lee, and Nick Whiteley. 2020. “Global Consensus Monte Carlo.” Journal of Computational and Graphical Statistics 30 (2): 249–59.

Rendon, Silvio R. 2013. “Fixed and Random Effects in Classical and Bayesian Regression.” Oxford Bulletin of Economics and Statistics 75 (3): 460–76.

Richardson, Sylvia, and Peter J Green. 1997. “On Bayesian Analysis of Mixtures with an Unknown Number of Components (with Discussion).” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59 (4): 731–92.

Rischard, Maxime, Zach Branson, Luke Bornn, and Luke W. Miratrix. 2020. “Do School Districts Affect NYC House Prices? Identifying Border Differences Using a Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs.” Journal of the American Statistical Association 115 (531): 1149–65. https://doi.org/10.1080/01621459.2019.1617155.

Robbins, Herbert, and Sutton Monro. 1951. “A Stochastic Approximation Method.” The Annals of Mathematical Statistics, 400–407.

Robert, Christian P., and George Casella. 2011. Monte Carlo Statistical Methods. 2nd ed. New York: Springer.

Robert, Christian P, and George Casella. 2010. Introducing Monte Carlo Methods with r. Vol. 18. Springer.

Roberts, G. O., A. Gelman, and W. R. Gilks. 1997. “Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms.” The Annals of Applied Probability 7 (1): 110–20.

Roberts, Gareth O., and Richard L. Tweedie. 1996. “Exponential Convergence of Langevin Distributions and Their Discrete Approximations.” Bernoulli 2 (4): 341–63. https://doi.org/10.2307/3318418.

Ročková, Veronika. 2020. “On Semi-Parametric Inference for BART.” In Proceedings of the 37th International Conference on Machine Learning, edited by Hal Daumé III and Aarti Singh, 119:8137–46. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v119/rockova20a.html.

Ročková, Veronika, and Edward I. George. 2018. “The Spike-and-Slab LASSO.” Journal of the American Statistical Association 113 (521): 431–44. https://doi.org/10.1080/01621459.2016.1260469.

Ročková, Veronika, and Stephanie van der Pas. 2020. “Posterior Concentration for Bayesian Regression Trees and Forests.” The Annals of Statistics 48 (4): 2103–30. https://doi.org/10.1214/19-AOS1879.

Ročková, Veronika, and Enakshi Saha. 2019. “On Theory for BART.” In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, edited by Kamalika Chaudhuri and Masashi Sugiyama, 89:2839–48. Proceedings of Machine Learning Research. PMLR.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55. https://doi.org/10.1093/biomet/70.1.41.

Rossi, P. 2017. Package Bayesm. https://CRAN.R-project.org/package=bayesm.

Rossi, Peter E. 2014. Bayesian Non- and Semi-Parametric Methods and Applications. Princeton, NJ: Princeton University Press. https://ideas.repec.org/b/pup/pbooks/10259.html.

Rossi, Peter E, Greg M Allenby, and Rob McCulloch. 2012. Bayesian Statistics and Marketing. John Wiley & Sons.

Roth, Jonathan, Pedro H. C. Sant’Anna, Alyssa Bilinski, and John Poe. 2023. “What’s Trending in Difference‐in‐differences? A Synthesis of the Recent Econometrics Literature.” Journal of Econometrics 235 (2): 2218–44.

Roy, Andrew Donald. 1951. “Some Thoughts on the Distribution of Earnings.” Oxford Economic Papers 3 (2): 135–46.

Rubin, Donald B. 1984. “Bayesianly Justifiable and Relevant Frequency Calculations for the Applies Statistician.” The Annals of Statistics 12 (4): 1151–72.

Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5): 688–701. https://doi.org/10.1037/h0037350.

———. 1978. “Bayesian Inference for Causal Effects: The Role of Randomization.” The Annals of Statistics 6 (1): 34–58. https://doi.org/10.1214/aos/1176344064.

———. 1988. “Using the SIR Algorithm to Simulate Posterior Distributions.” In Bayesian Statistics 3, edited by J. M. Bernardo, M. H. DeGroot, D. V. Lindley, and A. F. M. Smith, 395–402. Oxford University Press.

———. 1990. “Neyman (1923) and Causal Inference in Experiments and Observational Studies.” Statistical Science 5 (4): 472–80. https://doi.org/10.1214/ss/1177012032.

———. 2004. “Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies.” Journal of Educational and Behavioral Statistics 29 (3): 343–67. https://doi.org/10.3102/10769986029003343.

Rubin, Donnald B. 1981. “The Bayesian Bootstrap.” The Annals of Statistics 9 (1): 130–34.

Rue, Håvard, Sara Martino, and Nicolas Chopin. 2009. “Approximate Bayesian Inference for Latent Gaussian Models by Using Integrated Nested Laplace Approximations.” Journal of the Royal Statistical Society Series B: Statistical Methodology 71 (2): 319–92.

Rue, Håvard, Andrea Riebler, Sigrunn H Sørbye, Janine B Illian, Daniel P Simpson, and Finn K Lindgren. 2017. “Bayesian Computing with INLA: A Review.” Annual Review of Statistics and Its Application 4 (1): 395–421.

Saarela, Olli, Léo R. Belzile, and David A. Stephens. 2016. “A Bayesian View of Doubly Robust Causal Inference.” Biometrika 103 (3): 667–81. https://doi.org/10.1093/biomet/asw025.

Sargan, John Denis. 1958. “The Estimation of Economic Relationships Using Instrumental Variables.” Econometrica 26 (3): 393–415. https://doi.org/10.2307/1907619.

Savage, L. J. 1954. The Foundations of Statistics. New York: John Wiley & Sons, Inc.

Schennach, Susanne M. 2005. “Bayesian Exponentially Tilted Empirical Likelihood.” Biometrika 92 (1): 31–46. https://doi.org/10.1093/biomet/92.1.31.

———. 2007. “Point Estimation with Exponentially Tilted Empirical Likelihood.” The Annals of Statistics 35 (2): 634–72. https://doi.org/10.1214/009053606000001208.

Schlaifer, Robert, and Howard Raiffa. 1961. Applied Statistical Decision Theory.

Schwarz, Gideon. 1978. “Estimating the Dimension of a Model.” The Annals of Statistics, 461–64.

Scott, Steven L., Alexander W. Blocker, Fernando V. Bonassi, Hugh A. Chipman, Edward I. George, and Robert E. McCulloch. 2016. “Bayes and Big Data: The Consensus Monte Carlo Algorithm.” International Journal of Management Science and Engineering Management 11 (2): 78–88. https://doi.org/10.1080/17509653.2016.1142191.

Scott, Steven L, Alexander W Blocker, Fernando V Bonassi, Hugh A Chipman, Edward I George, and Robert E McCulloch. 2022. “Bayes and Big Data: The Consensus Monte Carlo Algorithm.” In Big Data and Information Theory, 8–18. Routledge.

Sellke, Thomas, MJ Bayarri, and James O Berger. 2001. “Calibration of p Values for Testing Precise Null Hypotheses.” The American Statistician 55 (1): 62–71.

Selvin, Steve. 1975. “A Problem in Probability (Letter to the Editor).” The American Statistician 11 (1): 67–71.

Serafini, Francesco. 2019. “Multinomial Logit Models with INLA.” R-INLA Tutorial. Https://Inla.r-Inla-Download.org/r-Inla.org/Doc/Vignettes/Multinomial.pdf.

Serna Rodríguez, M., A. Ramírez Hassan, and A. Coad. 2019. “Uncovering Value Drivers of High Performance Soccer Players.” Journal of Sport Economics 20 (6): 819–49.

Sethuraman, J. 1994. “A Constructive Definition of Dirichlet Priors.” Statistica Sinica 4: 639–50.

Settles, Burr. 2012. Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers. https://doi.org/10.2200/S00429ED1V01Y201207AIM018.

Shephard, Neil. 1994. “Partial Non-Gaussian State Space.” Biometrika 81 (1): 115–31.

Shumway, Robert H., and David S. Stoffer. 2017. Time Series Analysis and Its Applications: With r Examples. 4th ed. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-52452-8.

Simmons, Susan J, Fang Fang, Qijun Fang, and Karl Ricanek. 2010. “Markov Chain Monte Carlo Model Composition Search Strategy for Quantitative Trait Loci in a Bayesian Hierarchical Model.” World Academy of Science, Engineering and Technology 63: 58–61.

Sims, Christopher A. 1980. “Macroeconomics and Reality.” Econometrica: Journal of the Econometric Society, 1–48.

Sisson, Scott A, Yanan Fan, and Mark M Tanaka. 2007. “Sequential Monte Carlo Without Likelihoods.” Proceedings of the National Academy of Sciences of the USA 104 (6): 1760–65.

Sisson, Scott A, Fan, Yanan, Beaumont, and Mark. 2018. Handbook of Approximate Bayesian Computation. CRC Press.

Smith, A. F. M. 1973. “A General Bayesian Linear Model.” Journal of the Royal Statistical Society. Series B (Methodological). 35 (1): 67–75.

Smith, Adrian FM, and Alan E Gelfand. 1992. “Bayesian Statistics Without Tears: A Sampling–Resampling Perspective.” The American Statistician 46 (2): 84–88.

Solak, Ercan, Roderick Murray-Smith, W. E. Leithead, D. J. Leith, and C. E. Rasmussen. 2003. “Derivative Observations in Gaussian Process Models of Dynamic Systems.” In Advances in Neural Information Processing Systems, 1033–40. MIT Press.

Sparapani, Rodney A., Charles Spanbauer, and Robert McCulloch. 2021. “Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART r Package.” Journal of Statistical Software 97 (1): 1–66. https://doi.org/10.18637/jss.v097.i01.

Spiegelhalter, David J, Nicola G Best, Bradley P Carlin, and Angelika Van Der Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society: Series b (Statistical Methodology) 64 (4): 583–639.

Staiger, Douglas, and James H. Stock. 1997. “Instrumental Variables Regression with Weak Instruments.” Econometrica 65 (3): 557–86. https://doi.org/10.2307/2171753.

Stan Development Team. 2017. “Shinystan: Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models.” http://mc-stan.org/.

———. 2024. “Stan Modeling Language Users Guide and Reference Manual, 2024.” http://mc-stan.org/.

Stigler, Stephen. 2018. “Richard Price, the First Bayesian.” Statistical Science 33 (1): 117–25.

Stock, James H., and Motohiro Yogo. 2005. “Asymptotic Distributions of Instrumental Variables Statistics with Many Instruments.” In Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, edited by Donald W. K. Andrews and James H. Stock, 109–20. Cambridge University Press.

Stuart, Andrew M., and Aretha L. Teckentrup. 2018. “Posterior Consistency for Gaussian Process Approximations of Bayesian Posterior Distributions.” Mathematics of Computation 87 (310): 721–53. https://doi.org/10.1090/mcom/3244.

Syring, Nicholas, and Ryan Martin. 2019. “Calibrating General Posterior Credible Regions.” Biometrika 106 (2): 479–86. https://doi.org/10.1093/biomet/asy068.

Tanner, M. A., and W. H. Wong. 1987. “The Calculation of Posterior Distributions by Data Augmentation.” Journal of the American Statistical Association 82 (398): 528–40.

Tavaré, Simon, David J Balding, Robert C Griffiths, and Peter Donnelly. 1997. “Inferring Coalescence Times from DNA Sequence Data.” Genetics 145 (2): 505–18.

Teh, Yee Whye, Alexandre H. Thiery, and Sebastian J. Vollmer. 2016. “Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics.” Journal of Machine Learning Research 17 (1): 193–225.

Thomas, Samuel, and Wanzhu Tu. 2021. “Learning Hamiltonian Monte Carlo in r.” The American Statistician 75 (4): 403–13.

Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B (Methodological) 58 (1): 267–88. https://www.jstor.org/stable/2346178.

Tierney, Luke. 1994. “Markov Chains for Exploring Posterior Distributions.” The Annals of Statistics, 1701–28.

Tierney, Luke, and Joseph B Kadane. 1986. “Accurate Approximations for Posterior Moments and Marginal Densities.” Journal of the American Statistical Association 81 (393): 82–86.

Tüchler, Regina. 2008. “Bayesian Variable Selection for Logistic Models Using Auxiliary Mixture Sampling.” Journal of Computational and Graphical Statistics 17 (1): 76–94.

Tversky, A., and D. Kahneman. 1974. “Judgement Under Uncertainty: Heuristics and Biases.” Science 185: 1124–31.

Van der Vaart, Aad W. 2000. Asymptotic Statistics. Vol. 3. Cambridge university press.

Van Hasselt, Martijn. 2011. “Bayesian Inference in a Sample Selection Model.” Journal of Econometrics 165 (2): 221–32.

Verdinelli, Isabella, and Larry Wasserman. 1995. “Computing Bayes Factors Using a Generalization of the Savage-Dickey Density Ratio.” Journal of the American Statistical Association 90 (430): 614–18.

Vollmer, Sebastian J., Konstantinos C. Zygalakis, and Yee Whye Teh. 2016. “Exploration of the (Non-)asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics.” Journal of Machine Learning Research 17 (159): 1–48.

Wainwright, Martin J, Michael I Jordan, et al. 2008. “Graphical Models, Exponential Families, and Variational Inference.” Foundations and Trends in Machine Learning 1 (1–2): 1–305.

Walker, Stephen G. 2004a. “Modern Bayesian Asymptotics.” Statistical Science, 111–17.

Walker, Stephen G. 2004b. “New Approaches to Bayesian Consistency.” Annals of Statistics 32 (5): 2028–43. https://doi.org/10.1214/009053604000000409.

Wang, Xiangyu, and David B Dunson. 2013. “Parallelizing MCMC via Weierstrass Sampler.” arXiv Preprint arXiv:1312.4605.

Wasserstein, Ronald L., and Nicole A. Lazar. 2016. “The ASA’s Statement on p–Values: Context, Process and Purpose.” The American Statistician.

Wegmann, Daniel, Christoph Leuenberger, and Laurent Excoffier. 2009. “Efficient Approximate Bayesian Computation Coupled with Markov Chain Monte Carlo Without Likelihood.” Genetics 182 (4): 1207–18.

Welling, Max, and Yee W Teh. 2011. “Bayesian Learning via Stochastic Gradient Langevin Dynamics.” In Proceedings of the 28th International Conference on Machine Learning (ICML-11), 681–88. Citeseer.

West, Mike, and Jeff Harrison. 2006. Bayesian Forecasting and Dynamic Models. Springer Science & Business Media.

Wilson, Andrew G., and Hannes Nickisch. 2015. “Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP).” In Proceedings of the 32nd International Conference on Machine Learning (ICML), 37:1775–84. PMLR.

Winkelmann, R. 2004. “Health Care Reform and the Number of Doctor Visits - An Econometric Analysis.” Journal of Applied Econometrics 19 (4): 455–72.

Wood, Simon N. 2010. “Statistical Inference for Noisy Nonlinear Ecological Dynamic Systems.” Nature 466 (7310): 1102–4.

Woodward, P. 2005. “BugsXLA: Bayes for the Common Man.” Journal of Statistical Software 14 (5): 1–18.

Wooldridge, Jeffrey M. 2010. Econometric Analysis of Cross Section and Panel Data. MIT press.

Wooldridge, Jeffrey M. 2012. Introductory Econometrics: A Modern Approach. Fifth. Mason, Ohio: South-Western: Cengage Learning.

———. 2016. Introductory Econometrics: A Modern Approach. 6th ed. Boston, MA: Cengage Learning.

Woźniak, Tomasz. 2016. “Bayesian Vector Autoregressions.” Australian Economic Review 49 (3): 365–80.

———. 2024. Bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. https://doi.org/10.32614/CRAN.package.bsvars.

Wu, Changye, and Christian P Robert. 2017. “Average of Recentered Parallel MCMC for Big Data.” arXiv Preprint arXiv:1706.04780.

Xiaolei, Wang, and Tomasz Woźniak. 2024. bsvarSIGNs: Bayesian SVARs with Sign, Zero, and Narrative Restrictions. https://doi.org/10.32614/CRAN.package.bsvarSIGNs.

Yao, Yuling. 2021. “Bayesian Aggregation.” In Wiley StatsRef: Statistics Reference Online, 1–13. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118445112.stat08301.

Yao, Yuling, Aki Vehtari, Daniel Simpson, and Andrew Gelman. 2018. “Using Stacking to Average Bayesian Predictive Distributions.” Bayesian Analysis 13 (3): 917–1003. https://doi.org/10.1214/17-BA1091.

Yiu, Andrew, Robert J. B. Goudie, and Brian D. M. Tom. 2020. “Inference Under Unequal Probability Sampling with the Bayesian Exponentially Tilted Empirical Likelihood.” Biometrika 107 (4): 857–73. https://doi.org/10.1093/biomet/asaa028.

Zellner, Arnold. 1986. “On Assessing Prior Distributions and Bayesian Regression Analysis with g-Prior Distributions.” Bayesian Inference and Decision Techniques.

———. 1996a. “Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models.” In Modelling and Prediction Honoring Seymour Geisser, edited by Jack C. Lee, Wesley O. Johnson, and Arnold Zellner, 61–72. New York, NY: Springer. https://doi.org/10.1007/978-1-4612-2414-3_4.

———. 1996b. “Introduction to Bayesian Inference in Econometrics.”

Zhang, Fengshuo, and Chao Gao. 2020. “Convergence Rates of Variational Posterior Distributions.” The Annals of Statistics 48 (4): 2180–2207.

Zhang, Tong. 2006. “From \(\\varepsilon\)-Entropy to KL-Entropy: Analysis of Minimum Information Complexity Density Estimation.” Annals of Statistics 34 (5): 2180–210. https://doi.org/10.1214/009053606000000704.

Ziliak, S. 2008. “Guinnessometrics; the Economic Foundation of Student’s t.” Journal of Economic Perspectives 22 (4): 199–216.

Zou, Hui, and Trevor Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2): 301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x.