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“Bayesianism” books center on the Bayesian approach to reasoning and decision-making under uncertainty, where degrees of belief are represented as probabilities and updated with evidence via Bayes’ theorem. The category spans accessible introductions to Bayesian thinking, practical guides to data analysis and modeling, and advanced texts on theory and computation. Topics commonly include priors and posteriors, predictive distributions, model comparison (e.g., Bayes factors), hierarchical models, Bayesian networks, causal inference, and uncertainty quantification, alongside computational methods such as Markov chain Monte Carlo, variational inference, and probabilistic programming. Works range across statistics, machine learning, econometrics, psychology and neuroscience (e.g., the “Bayesian brain”), and the philosophy of science, including debates over subjective versus objective Bayes and the role of Bayesianism in rational inference.

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