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“Posterior Distributions” is a category for books centered on Bayesian thinking—how we update prior beliefs with data to obtain posterior conclusions. It includes accessible introductions to Bayesian inference, rigorous treatments of probability and conjugacy, and modern computational tools such as MCMC, variational inference, and approximate Bayesian computation, often with hands-on guides in Stan, PyMC, R, or Python. Titles span applications across science, social science, engineering, medicine, and finance, and cover advanced topics like hierarchical modeling, model comparison, decision-making under uncertainty, and causal inference. Readers will learn how to interpret and communicate results via credible intervals, posterior predictive checks, and sensitivity analyses, making this shelf a practical bridge from statistical theory to real-world inference.

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