Titlebook: Learning with the Minimum Description Length Principle; Kenji Yamanishi Book 2023 Springer Nature Singapore Pte Ltd. 2023 Minimum Descript

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,Information and Coding,and coding are equivalent through the Kraft inequality. The most primitive quantification of information is Shannon’s information, which is the optimal code-length when a probability distribution is known in advance. We introduce the notion of stochastic complexity (SC) as an extension of Shannon’s

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Parameter Estimation,tal task of statistical inference, which should be addressed preceding model selection, as focused on in the subsequent chapters. Therefore, this chapter can be thought of as a preliminary chapter for model selection in this book. The methods of the maximum likelihood estimation (MLE), maximum a pos

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Latent Variable Model Selection,del selection in latent variable models; the latent stochastic complexity (LSC) and decomposed normalized maximum likelihood code-length (DNML). It is shown how DNML can be applied to model selection for typical latent variable model classes such as naïve Bayes models, latent Dirichlet allocation mo

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Sequential Prediction, reduced to the problem of minimizing the cumulative code-length when the code-length is calculated sequentially. We consider three types of prediction algorithms; maximum likelihood prediction algorithm, Bayesian prediction algorithm, and sequentially normalized maximum likelihood algorithm. We giv

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MDL Change Detection,e the MDL principle plays a key role in designing effective change detection algorithms. We classify the change detection issues into parameter change detection and latent structure change detection. Each of both issues is further classified to abrupt change detection and gradual change detection. W

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