Titlebook: Introducing Monte Carlo Methods with R; Christian Robert,George Casella Textbook 2010 Springer-Verlag New York 2010 Markov chain.Mathemati

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书目名称Introducing Monte Carlo Methods with R读者反馈学科排名

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Monte Carlo Optimization,l maxima (or minima) and are sufficiently attracted by the global maximum (or minimum). The second use, described in Section 5.4, is closer to Chapter 3 in that simulation is used to approximate the function to be optimized.

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Convergence Monitoring and Adaptation for MCMC Algorithms,separate notions of convergence, namely convergence to stationarity and convergence of ergodic average, in contrast with iid settings. We also discuss several types of convergence diagnostics, primarily those contained in the coda package of Plummer et al. (2006), even though more accurate methods may be available in specific settings.

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Textbook 2010ians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. .Introducing Monte Carlo Methods with R. covers the main tools used in statistical simulation from a programmer‘s poin

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Random Variable Generation,r program. Given the availability of a uniform generator in R, as explained in Section 2.1.1, we do not deal with the specific production of uniform random variables. The most basic techniques relate the distribution to be simulated to a uniform variate by a transform or a particular probabilistic p

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Monte Carlo Integration,te Carlo methods; that is, taking advantage of the availability of computer-generated random variables to approximate univariate and multidimensional integrals. In Section 3.2, we introduce the basic notion of Monte Carlo approximations as a by-product of the Law of Large Numbers, while Section 3.3