Bayesian Statistics

Bayesian Statistics. Priors, posteriors, hierarchical models, and Bayesian computation. The literature on bayesian statistics divides naturally along several axes: the foundational structures that organise the subject, the techniques that drive proofs and computations, the questions about classification or representation that animate current research, and the bridges to neighbouring areas of mathematics and science. The references below trace those axes through the canonical textbook treatments and recent technical contributions.

Foundations and canonical references

The standard treatments of bayesian statistics approach the subject from complementary angles. Gelman, Bayesian Data Analysis (2013) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to. Robert, The Bayesian Choice (2007) gives a parallel, more proof-oriented exposition of the same material and is widely used as a graduate text. Bishop, Pattern Recognition and Machine Learning (2006) offers an alternative presentation that complements the primary references and is useful for triangulating definitions and proof techniques.

Open methodological questions for bayesian statistics include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.