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Statistical Modeling and Computation

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概要 This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computationprovides a unique introduction to modern S...tatistics from both classical and Bayesian perspectives. It also offersan 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 authorsaddress 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 authorsinclude 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.続きを見る
目次 Probability Models
Random Variables and Probability Distributions
Joint Distributions
Common Statistical Models
Statistical Inference
Likelihood
Monte Carlo Sampling
Bayesian Inference
Generalized Linear Models
Dependent Data Models
State Space Models
References
Solutions
MATLAB Primer
Mathematical Supplement
Index.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2014 English/International

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登録日 2020.06.27
更新日 2020.06.28