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"Parametric models" as a book category covers texts that develop and apply mathematical models defined by a finite set of parameters — models whose form is specified in advance and whose unknowns are captured by parameters to be estimated from data. This includes classical and modern treatments of linear and generalized linear models, exponential-family and mixture models, time series and state-space models, parametric machine learning models (e.g., logistic regression, classical neural networks), and the theoretical foundations of estimation, identifiability, likelihood-based and Bayesian inference, hypothesis testing, and model selection. Books in this category range from introductory how‑to guides on fitting and interpreting parametric models to advanced monographs on asymptotic theory, computational algorithms (EM, MCMC, optimization), and domain-specific applications in economics, biology, engineering and the physical sciences. Ideal for students, statisticians, data scientists and applied researchers, the category emphasizes structured, interpretable modeling frameworks where parameter estimation and uncertainty quantification are central.

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