Information and complexity in statistical modeling
著者
書誌事項
Information and complexity in statistical modeling
（Information science and statistics / series editors M. Jordan ... [et al.]）
Springer, c2007
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注記
Includes bibliographical reference and index
内容説明・目次
内容説明
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
目次
Information and Coding.- Shannon-Wiener Information.- Coding of Random Processes.- Statistical Modeling.- Kolmogorov Complexity.- Stochastic Complexity.- Structure Function.- Optimally Distinguishable Models.- The MDL Principle.- Applications.
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