Introduction to Statistical Decision Theory

摘要: The Bayesian revolution in statistics -- where is integrated with decision making areas such as management, public policy, engineering, and clinical medicine here to stay. Introduction Statistical Decision Theory states the case a self-contained, comprehensive way shows how approach operational relevant for real-world under uncertainty. Starting an extensive account of foundations theory, authors develop intertwining concepts subjective probability utility. They then systematically comprehensively examine Bernoulli, Poisson, Normal (univariate multivariate) data generating processes. For each process they consider prior judgments about uncertain parameters are modified given results statistical sampling, investigate typical problems which main sources uncertainty population parameters. also discuss value sampling information optimal sample sizes costs economics terminal problems. Unlike most introductory texts statistics, integrates inference discusses actions involving economic payoffs risks. After developing rationale demonstrating power relevance subjective, approach, text examines critiques limitations objective, classical approach.