Degroot and schervish probability and statistics

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degroot and schervish probability and statistics

Probability and Statistics by Morris H. DeGroot

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a new chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), expanded coverage of residual analysis in linear models, and more examples using real data. Probability & Statistics was written for a one or two semester probability and statistics course offered primarily at four-year institutions and taken mostly by sophomore and junior level students, majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus. Introduction to Probability; Conditional Probability; Random Variables and Distribution; Expectation; Special Distributions; Estimation; Sampling Distributions of Estimators; Testing Hypotheses; Categorical Data and Nonparametric Methods; Linear Statistical Models; Simulation For all readers interested in probability and statistics.
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Published 02.12.2018

Andrew Gelman - Bayes, statistics, and reproducibility (Rutgers, Foundations of Probability)

Probability and Statistics

DeGroot joined Carnegie Mellon in and became a University Professor, the school's highest faculty position. He was the founding editor of the review journal Statistical Science. He wrote six books, edited four volumes and authored over one hundred papers. Most of his research was on the theory of rational decision-making under uncertainty. His Optimal Statistical Decisions , published in , is still recognized as one of the great books in the field. His courses on statistical decision theory taught at Carnegie-Mellon influenced Edward C. Prescott and Robert Lucas, Jr.

The revision of this well-respected text presents a 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. Convert currency. Add to Basket. Book Description Pearson.

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Probability and Statistics (4th Edition)

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    scopenitout.com: Probability and Statistics (4th Edition) (): Morris H. DeGroot, Mark J. Schervish: Books.

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