Alquier, Pierre. 2021. “User-Friendly Introduction to PAC-Bayes Bounds.” arXiv:2110.11216 [Cs, Math, Stat]
Bacchus, F, H E Kyburg, and M Thalos. 1990. “Against Conditionalization.” Synthese 85 (3): 475–506.
Bernardo, José M., and Adrian F. M. Smith. 2000. Bayesian Theory. 1 edition. Chichester: Wiley.
Carpenter, Bob, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt. 2015. “The Stan Math Library: Reverse-Mode Automatic Differentiation in C++.” arXiv Preprint arXiv:1509.07164
Caruana, Rich. 1998. “Multitask Learning.”
In Learning to Learn
, 95–133. Springer, Boston, MA.
Deisenroth, Marc, and Stefanos Zafeiriou. 2017. “Mathematics for Inference and Machine Learning.” Dept. Comput., Imperial College London, London, UK, Tech. Rep., Accessed on Jul, 126.
Diaconis, Persi, and Donald Ylvisaker. 1979. “Conjugate Priors for Exponential Families.” The Annals of Statistics
7 (2): 269–81.
Gabry, Jonah, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman. 2019. “Visualization in Bayesian Workflow.” Journal of the Royal Statistical Society: Series A (Statistics in Society)
182 (2): 389–402.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis
. 3 edition. Chapman & Hall/CRC texts in statistical science. Boca Raton: Chapman and Hall/CRC.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2021. Regression and other stories. Cambridge, UK: Cambridge University Press.
Gelman, Andrew, and Deborah Nolan. 2017. Teaching Statistics: A Bag of Tricks. 2 edition. Oxford: Oxford University Press.
Gelman, Andrew, and Donald B. Rubin. 1995. “Avoiding Model Selection in Bayesian Social Research.” Sociological Methodology
Gelman, Andrew, and Cosma Rohilla Shalizi. 2013. “Philosophy and the Practice of Bayesian Statistics.” British Journal of Mathematical and Statistical Psychology
66 (1): 8–38.
Gelman, Andrew, and Yuling Yao. 2021. “Holes in Bayesian Statistics.” Journal of Physics G: Nuclear and Particle Physics
48 (1): 014002.
Goodman, Noah, Vikash Mansinghka, Daniel Roy, Keith Bonawitz, and Daniel Tarlow. 2012. “Church: A Language for Generative Models.” arXiv:1206.3255
Goodrich, Ben, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Bob Carpenter, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “Stan : A Probabilistic Programming Language.” Journal of Statistical Software
Hubbard, Douglas W. 2014. How to Measure Anything: Finding the Value of Intangibles in Business. 3 edition. Hoboken, New Jersey: Wiley.
Li, Meng, and David B. Dunson. 2016. “A Framework for Probabilistic Inferences from Imperfect Models.” arXiv:1611.01241 [Stat]
Linden, Sander van der, and Breanne Chryst. 2017. “No Need for Bayes Factors: A Fully Bayesian Evidence Synthesis.” Frontiers in Applied Mathematics and Statistics
ma, wei jin, Konrad Paul Kording, and Daniel Goldreich. n.d. Bayesian Models of Perception and Action
MacKay, David JC. 1999. “Comparison of Approximate Methods for Handling Hyperparameters.” Neural Computation
11 (5): 1035–68.
Mandt, Stephan, Matthew D. Hoffman, and David M. Blei. 2017. “Stochastic Gradient Descent as Approximate Bayesian Inference.” JMLR
Martin, Gael M., David T. Frazier, and Christian P. Robert. 2020. “Computing Bayes: Bayesian Computation from 1763 to the 21st Century.” arXiv:2004.06425 [Stat]
Raftery, Adrian E. 1995. “Bayesian Model Selection in Social Research.” Sociological Methodology
Robert, Christian P. 2007. The Bayesian choice: from decision-theoretic foundations to computational implementation. 2nd ed. Springer texts in statistics. New York: Springer.
Schervish, Mark J. 2012. Theory of Statistics
. Springer Series in Statistics. New York, NY: Springer Science & Business Media.
Schoot, Rens van de, Sarah Depaoli, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, et al. 2021. “Bayesian Statistics and Modelling.” Nature Reviews Methods Primers
1 (1): 1–26.
Stuart, A. M. 2010. “Inverse Problems: A Bayesian Perspective.” Acta Numerica
Zellner, Arnold. 1988. “Optimal Information Processing and Bayes’s Theorem.” The American Statistician
42 (4): 278–80.
———. 2002. “Information Processing and Bayesian Analysis.” Journal of Econometrics
, Information and Entropy Econometrics, 107 (1): 41–50.