Statistical Modeling and Computation (Paperback, Softcover reprint of the original 1st ed. 2014)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	August 2016
First published:	2014
Authors:	Dirk P. Kroese • Joshua C. C. Chan
Dimensions:	235 x 155 x 24mm (L x W x T)
Format:	Paperback
Pages:	400
Edition:	Softcover reprint of the original 1st ed. 2014
ISBN-13:	978-1-4939-5332-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	1-4939-5332-X
Barcode:	9781493953325

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