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Statistical modeling and computation / Dirk P. Kroese and Joshua C.C. Chan.

By: Contributor(s): Material type: TextTextPublication details: New York : Springer, 2014.Description: xx, 400 p. : illustrations ; 24 cmISBN:
  • 9781461487746 (hbk. : alk. paper)
Subject(s): DDC classification:
  • 23 K93 000SA.055
Contents:
Part I Fundamentals of Probability 1. Probability Models -- 2. Random Variables and Probability Distributions -- 3. Joint Distributions -- Part II Statistical Modeling and Classical and Bayesian Inference 4. Common Statistical Models -- 5. Statistical Inference -- 6. Likelihood -- 7. Monte Carlo Sampling -- 8. Bayesian Inference -- Part III Advanced Models and Inference 9. Generalized Linear Models -- 10. Dependent Data Models -- 11. State Space Models -- A. Matlab Primer -- B. Mathematical Supplement-- References-- Solutions-- Index--
Summary: This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.055 K93 (Browse shelf(Opens below)) Available 135377
Total holds: 0

Includes bibliographical references and index.

Part I Fundamentals of Probability
1. Probability Models --
2. Random Variables and Probability Distributions --
3. Joint Distributions --

Part II Statistical Modeling and Classical and Bayesian Inference
4. Common Statistical Models --
5. Statistical Inference --
6. Likelihood --
7. Monte Carlo Sampling --
8. Bayesian Inference --

Part III Advanced Models and Inference
9. Generalized Linear Models --
10. Dependent Data Models --
11. State Space Models --

A. Matlab Primer --
B. Mathematical Supplement--

References--
Solutions--
Index--

This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.

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