Books like Bayesian statistics by Peter M. Lee

A practical guide to Bayesian statistical analysis that prioritizes computation, model checking, and data‑collection design, with examples spanning regression, hierarchical, robust, generalized linear, and mixture models.

Elementary Bayesian Statistics is a thorough and easily accessible introduction to the theory and practical application of Bayesian statistics. It presents methods to assist in the collection, summary and presentation of numerical data. Bayesian statistics are becoming an increasingly important and more frequently used method for analysing statistical data. The author defines concepts and methods with a variety of examples and uses a stage-by-stage approach to coach the reader through the applied examples. Also included are a wide range of problems to challenge the reader and the book makes extensive use of Minitab to apply computational techniques to statistical problems. Issues covered include probability, Bayes's Theorem and categorical states, frequency, the Bernoulli process and Poisson process, estimation, testing hypotheses and the normal process with known parameters and uncertain parameters. Elementary Bayesian Statistics will be an essential resource for students as a supplementary text in traditional statistics courses. It will also be welcomed by academics, researchers and econometricians wishing to know more about Bayesian statistics.

Revised edition of DeGroot’s classic, blending classical and Bayesian statistics with new chapters on simulation—MCMC and Bootstrap—and enhanced residual analysis for linear models, all illustrated with real‑world data.