Titlebook: Bayesian Computation with R; Jim Albert Textbook 2009Latest edition Springer-Verlag New York 2009 Bayesian Inference.Hierarchical modeling

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書目名稱Bayesian Computation with R影響因子(影響力)




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書目名稱Bayesian Computation with R網(wǎng)絡(luò)公開度




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Introduction to Bayesian Thinking,rtion. Before taking data, one has beliefs about the value of the proportion and one models his or her beliefs in terms of a prior distribution. We will illustrate the use of different functional forms for this prior. After data have been observed, one updates one’s beliefs about the proportion by c

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Single-Parameter Models,sian inference for a variance for a normal population and inference for a Poisson mean when informative prior information is available. For both problems, summarization of the posterior distribution is facilitated by the use of R functions to compute and simulate distributions from the exponential f

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Markov Chain Monte Carlo Methods,ior distribution, but it can be difficult to set up since it requires the construction of a suitable proposal density. Importance sampling and SIR algorithms are also general-purpose algorithms, but they also require proposal densities that may be difficult to find for high-dimensional problems. In

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