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“Model Calibration” as a book category focuses on the principles and practices of tuning mathematical and computational models so their outputs align with observed data, enabling trustworthy prediction and decision-making. Titles in this genre cover parameter estimation and uncertainty quantification, from classical least squares and maximum likelihood to Bayesian calibration, MCMC, and filtering methods (e.g., Kalman and particle filters), as well as surrogate and adjoint approaches for complex models. They emphasize identifiability, sensitivity analysis, experimental design for informative data, and rigorous validation and diagnostics to avoid overfitting and assess credibility. Many books frame calibration as an inverse problem or data assimilation task and provide software workflows and case studies across engineering, climate and hydrology, epidemiology, finance, and machine learning. The audience includes researchers and practitioners who build or use models and need to reconcile theory with empirical evidence.

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