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“Prior Probability” as a book category features works on how we represent and reason about uncertainty before observing data—the core of Bayesian thinking. It spans accessible introductions and rigorous texts on Bayesian inference, elicitation and choice of priors, subjective vs. objective approaches, and decision theory. The category also includes practical guides and case studies showing how prior assumptions shape conclusions in statistics, data science, AI, medicine, economics, and policy, alongside popular science and philosophical treatments of belief, evidence, and rational updating. Collectively, these books teach readers to articulate, justify, and stress-test priors, and to make better forecasts and decisions under uncertainty.

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