This textbook for first-year graduate students reveals the theory of probability and statistical inference using worked examples, exercises, and computer simulations. Mukhopadhyay (University of Connecticut) first introduces the basic ideas and techniques in probability theory, then studies more rigorous topics such as the Helmert transformation for normal distributions; convergence in probability and distribution; the central limit theorem for the sample variance; sample distributions and the Cornish-Fisher expansions; the fundamentals of sufficiency, information, completeness, and ancillary; Basu's Theorem; maximum likelihood estimators (MLEs); the Neyman- Pearson theory of most powerful (MP); Bayesian methods; and variance stabilizing transformations. Annotation c. Book News, Inc., Portland, OR (booknews.com)