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Probabilistic reasoning as a book category covers methods and ideas for thinking, modeling, and making decisions under uncertainty using probability as the formal language; it spans introductory and advanced texts on probability theory, Bayesian inference, graphical models (e.g., Bayesian networks), probabilistic programming, Monte Carlo methods, and decision theory. Titles range from popular explanations of how humans and machines reason with uncertain information to practical guides for building probabilistic models in AI, statistics, finance, medicine, and engineering, as well as rigorous mathematical monographs on inference and learning. Books in this category often emphasize modeling assumptions, updating beliefs with evidence, quantifying and propagating uncertainty, and applied techniques for estimation and prediction, and they target audiences from curious general readers to practitioners and researchers.

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