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Bayesian statistics books focus on the Bayesian approach to learning from data, where prior beliefs are combined with observed evidence via Bayes’ theorem to produce posterior distributions for parameters and predictions. The category includes introductory texts covering priors, likelihoods, posterior inference, credible intervals, and decision theory; intermediate and advanced treatments of hierarchical/multilevel models, model comparison, nonparametrics, and uncertainty quantification; and computational works on MCMC, variational inference, and sequential methods, often using tools like Stan, PyMC, JAGS, or BUGS. Titles range from theory and methodology to practical guides and case studies, with applications across science, engineering, medicine, economics, social science, and machine learning. These books serve students, researchers, and practitioners seeking principled, transparent inference and decision-making under uncertainty.

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