Monte Carlo statistical methods
著者
書誌事項
Monte Carlo statistical methods
（Springer texts in statistics）
Springer, 1999
- : hbk
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注記
Includes bibliographical references (p. [459]-487) and indexes
内容説明・目次
内容説明
Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computation, but may not be a close representation of a realistic model. This dilemma is present in many branches of statistical applications, for example in electrical engineering, aeronautics, biology, networks, and astronomy. Markov chain Monte Carlo methods have been developed to provide realistic models.
目次
- Introduction
- Random Variable Generation
- Monte Carlo Integration
- Markov Chains
- Monte Carlo Optimization
- The Metropolis-Hastings Algorithm
- The Gibbs Sampler
- Diagnosing Convergence
- Implementation in Missing Data Models
- Probability Distributions
- Notation
- References
- Author Index
- Subject Index.
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