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- W4386742405 abstract "In this chapter, we introduce some methodologies for parameter estimation on the basis of the MDL principle. Parameter estimation is the most fundamental 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 posteriori estimation (MAP), and Bayes estimation are introduced. These estimation methods may often be computationally difficult. In order to overcome the computational problems, we show the expectation and maximization (EM) algorithm, gradient descent method, and Markov chain Monte Carlo method. Specifically, along with the MAP estimation, we further introduce the notions of sparse regularization and its extension to penalty selection." @default.
- W4386742405 created "2023-09-15" @default.
- W4386742405 creator A5021981442 @default.
- W4386742405 date "2023-01-01" @default.
- W4386742405 modified "2023-10-01" @default.
- W4386742405 title "Parameter Estimation" @default.
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- W4386742405 doi "https://doi.org/10.1007/978-981-99-1790-7_2" @default.
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