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"Exponential families" as a book category would encompass works focused on the class of probability distributions that can be expressed in exponential-family form and the mathematical, statistical and applied ideas that arise from them. Books in this category typically cover canonical examples (Bernoulli, Binomial, Poisson, Gaussian, exponential), properties such as sufficient statistics, conjugate priors, maximum likelihood and Fisher information, theory of generalized linear models, and links to information geometry and convex analysis. They range from rigorous theoretical monographs exploring measure-theoretic foundations and asymptotics to practical guides emphasizing inference, model-fitting, and computational methods (EM, variational inference, Bayesian conjugacy). Intended readers include advanced undergraduates, graduate students, and researchers in statistics, machine learning and applied fields who need a unified framework for modeling, inference, and both classical and Bayesian methods.

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