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Statistical decision theory as a book category covers the theory and methods for making optimal or principled choices under uncertainty, combining probability, loss/utility modeling, estimation, hypothesis testing, and decision rules. Books in this category range from rigorous theoretical treatments of Bayes and frequentist approaches (risk, admissibility, minimax, complete class theorems) to applied texts on sequential decision making, empirical Bayes, robust and cost-sensitive procedures, and computational techniques (dynamic programming, MCMC, Monte Carlo). They are aimed at advanced undergraduates, graduate students, and practitioners in statistics, data science, economics, engineering and machine learning who have a grounding in probability, calculus and linear algebra. Typical volumes include mathematical foundations, examples and exercises, algorithmic implementations, and case studies illustrating real-world decision problems in medicine, finance, engineering and AI.

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