Titlebook: Handbook of Computational Statistics; Concepts and Methods James E. Gentle,Wolfgang Karl H?rdle,Yuichi Mori Book 2012Latest edition Springe

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Random Number Generation and powerful mathematical theory, but exact implementation of these concepts on conventional computers seems impossible. In practice, random variables and other random objects are . by .. The purpose of these algorithms is to produce sequences of numbers or objects whose behavior is very hard to di

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Markov Chain Monte Carlo Technologyability distribution that is based on Markov chains whose stationary distribution is the probability distribution of interest. This class of methods, popularly referred to asMarkov chain Monte Carlo methods, or simply MCMC methods, have been influential in the modern practice of Bayesian statistics

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The EM Algorithmriety of incomplete-data problems. The EM algorithm has a number of desirable properties, such as its numerical stability, reliable global convergence, and simplicity of implementation. There are, however, two main drawbacks of the basic EM algorithm – lack of an in-built procedure to compute the co

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Stochastic Optimizationtandard approaches for solving challenging optimization problems.This chapter provides a synopsis of some of the critical issues associatedwith stochastic optimization and a gives a summary of several popularalgorithms. Much more complete discussions are available in the indicatedreferences.

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