痛得哭了 发表于 2025-3-26 22:56:28

Regression Models,el and describe algorithms to simulate from the joint distribution of regression parameters and error variance and the predictive distribution of future observations. One can judge the adequacy of the fitted model through use of the posterior predictive distribution and the inspection of the posteri

的染料 发表于 2025-3-27 04:22:31

Gibbs Sampling,ppose that we partition the parameter vector of interest into . components . = (.1.), where . may consist of a vector of parameters. The MCMC algorithm is implemented by sampling in turn from the . conditional posterior distributions.

poliosis 发表于 2025-3-27 07:26:39

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LAIR 发表于 2025-3-27 10:05:27

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赞美者 发表于 2025-3-27 16:55:02

Claus Hüsselmann,Thomas Hemmannsian 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

安慰 发表于 2025-3-27 18:51:41

Claus Hüsselmann,Thomas Hemmannation or multinomial parameters, posterior inference is accomplished by simulating from distributions of standard forms. Once a simulated sample is obtained from the joint posterior, it is straightforward to perform transformations on these simulated draws to learn about any function of the paramete

insurgent 发表于 2025-3-28 00:23:53

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误传 发表于 2025-3-28 06:10:56

Rolf Irion,Fabian Schmidt-Schröderrior 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

倔强一点 发表于 2025-3-28 08:17:19

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减少 发表于 2025-3-28 12:26:59

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查看完整版本: Titlebook: Bayesian Computation with R; Jim Albert Textbook 20071st edition Springer-Verlag New York 2007 Bayesian Inference.Hierarchical modeling.Ma