Probability and Statistics 4th Edition PDF Download Ebook. Morris H. DeGroot and Mark J. Schervish offer balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data.

Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. A new chapter on simulation has been added. This includes methods for simulating specific distributions, importance sampling, Markov chain Monte Carlo, and the bootstrap.

New sections or subsections on conditionally independent events and random variables, the log normal distribution, quantiles, prediction and prediction intervals, improper priors, Bayes tests, power functions, M-estimators, residual plots in linear models, and Bayesian analysis of simple linear regression are now included.

Brief introductions and summaries have been added to each technical section. The introductory paragraphs give readers a hint about what they are going to encounter. The summaries list the most important ideas. The author has added special notes where it is useful to briefly summarize or make a connection to a point made elsewhere in the text.

Some material has been reorganized. Independence is now introduced after conditional probability. The first five chapters of the text are devoted to probability and can serve as the text for a one-semester course on probability. In addition to examples using current data, some elementary concepts of probability are illustrated by famous examples such as the birthday problem, the tennis tournament problem, the matching problem, and the collector's problem.

Included as a special feature are sections on Markov chains, the Gambler's Ruin problem, and utility and preferences among gambles. These topics are treated in a completely elementary fashion, and can be omitted without loss of continuity if time is limited. Optional sections of the book are indicated by an asterisk in the Table of Contents.

Chapters 6 through 10 are devoted to statistical inference. Both classical and Bayesian statistical methods are developed in an integrated presentation which will be useful to students when applying the concepts to the real world.

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