References

Belenky, Gregory, Nancy J. Wesensten, David R. Thorne, Maria L. Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo, and Thomas J. Balkin. 2003. “Patterns of Performance Degradation and Restoration During Sleep Restriction and Subsequent Recovery: A Sleep Dose‐response Study.” Journal of Sleep Research 12 (1): 1–12. https://doi.org/10.1046/j.1365-2869.2003.00337.x.
Betancourt, Michael. 2018. “A Conceptual Introduction to Hamiltonian Monte Carlo.” arXiv. https://doi.org/10.48550/arXiv.1701.02434.
Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Springer New York, NY. https://link.springer.com/book/9780387310732.
Cho, Kenta, and Bart Jacobs. 2019. “Disintegration and Bayesian Inversion via String Diagrams.” Mathematical Structures in Computer Science 29 (7): 938–71. https://doi.org/10.1017/S0960129518000488.
Darwiche, Adnan. 2009. “Modeling and Reasoning with Bayesian Networks,” April. https://doi.org/10.1017/CBO9780511811357.
Durbin, Richard, Sean R. Eddy, Anders Krogh, and Graeme Mitchison. 1998. “Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids,” April. https://doi.org/10.1017/CBO9780511790492.
Fong, Brendan. 2013. “Causal Theories: A Categorical Perspective on Bayesian Networks.” https://doi.org/10.48550/arXiv.1301.6201.
Fritz, Tobias. 2020. “A Synthetic Approach to Markov Kernels, Conditional Independence and Theorems on Sufficient Statistics.” Advances in Mathematics 370 (August): 107239. https://doi.org/10.1016/j.aim.2020.107239.
Fritz, Tobias, and Andreas Klingler. 2023. “The d-Separation Criterion in Categorical Probability.” Journal of Machine Learning Research 24 (46): 1–49. http://jmlr.org/papers/v24/22-0916.html.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. Chapman; Hall/CRC. https://doi.org/10.1201/b16018.
Gelman, Andrew, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. 2020. “Bayesian Workflow.” arXiv. https://doi.org/10.48550/ARXIV.2011.01808.
Hamilton, James D. 1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57 (2): 357. https://doi.org/10.2307/1912559.
Jacobs, Bart. n.d. Structured Probabilistic Reasoning. https://www.cs.ru.nl/B.Jacobs/PAPERS/ProbabilisticReasoning.pdf.
Kallioinen, Noa, Topi Paananen, Paul-Christian Bürkner, and Aki Vehtari. 2024. “Detecting and Diagnosing Prior and Likelihood Sensitivity with Power-Scaling.” Statistics and Computing 34 (1): 57. https://doi.org/10.1007/s11222-023-10366-5.
Kochenderfer, Mykel J., Tim A. Wheeler, and Kyle H. Wray. 2022. Algorithms for Decision Making. MIT Press. https://algorithmsbook.com/decisionmaking/.
Koller, Daphne, and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Lawvere, F. William. 1962. “The Category of Probabilistic Mappings.” https://lawverearchives.com/wp-content/uploads/2025/07/1962.probmap.pdf.
Loeliger, H. 2004. “An Introduction to Factor Graphs.” IEEE Signal Processing Magazine 21 (1): 28–41. https://doi.org/10.1109/MSP.2004.1267047.
Lorenz, Robin, and Sean Tull. 2023. “Causal Models in String Diagrams.” https://doi.org/10.48550/arXiv.2304.07638.
Martin, Osvaldo A., Oriol Abril-Pla, Jordan Deklerk, and ArviZ-devs. 2025. “Exploratory Analysis of Bayesian Models,” April. https://doi.org/10.5281/zenodo.15127548.
McElreath, Richard. 2018. Statistical Rethinking: A Bayesian Course with Examples in r and Stan. Chapman; Hall/CRC. https://doi.org/10.1201/9781315372495.
Meyn, Sean, Richard L. Tweedie, and Peter W. Glynn. 2009. Markov Chains and Stochastic Stability. 2nd ed. Cambridge University Press. https://doi.org/10.1017/CBO9780511626630.
Murphy, Kevin P. 2022. Probabilistic Machine Learning: An Introduction. MIT Press. http://probml.github.io/book1.
———. 2023. Probabilistic Machine Learning: Advanced Topics. MIT Press. http://probml.github.io/book2.
Pearl, Judea. 1988. Probabilistic Reasoning in Intelligent Systems. Elsevier. https://doi.org/10.1016/C2009-0-27609-4.
———. 2009. Causality: Models, Reasoning, and Inference. Cambridge University Press. https://doi.org/10.1017/CBO9780511803161.
Robert, Christian P., and George Casella. 2004. Monte Carlo Statistical Methods. Springer Texts in Statistics. New York, NY: Springer New York. https://doi.org/10.1007/978-1-4757-4145-2.
Russell, Stuart J, and Peter Norvig. 2021. Artificial Intelligence: A Modern Approach. 4th ed. Pearson.
Spirtes, Peter, Clark Glymour, and Richard Scheines. 2000. Causation, Prediction, and Search. 2nd ed. MIT Press.
Tierney, Luke. 1994. “Markov Chains for Exploring Posterior Distributions.” The Annals of Statistics 22 (4). https://doi.org/10.1214/aos/1176325750.