標題: Titlebook: Bayesian Computation with R; Jim Albert Textbook 2009Latest edition Springer-Verlag New York 2009 Bayesian Inference.Hierarchical modeling [打印本頁] 作者: 開脫 時間: 2025-3-21 19:09
書目名稱Bayesian Computation with R影響因子(影響力)
書目名稱Bayesian Computation with R影響因子(影響力)學科排名
書目名稱Bayesian Computation with R網(wǎng)絡公開度
書目名稱Bayesian Computation with R網(wǎng)絡公開度學科排名
書目名稱Bayesian Computation with R被引頻次
書目名稱Bayesian Computation with R被引頻次學科排名
書目名稱Bayesian Computation with R年度引用
書目名稱Bayesian Computation with R年度引用學科排名
書目名稱Bayesian Computation with R讀者反饋
書目名稱Bayesian Computation with R讀者反饋學科排名
作者: 失誤 時間: 2025-3-21 22:29
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作者: 修飾語 時間: 2025-3-22 03:15
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作者: Diluge 時間: 2025-3-22 05:54 作者: 膽小鬼 時間: 2025-3-22 12:38
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 作者: harpsichord 時間: 2025-3-22 15:41 作者: 蒸發(fā) 時間: 2025-3-22 20:52 作者: OREX 時間: 2025-3-23 00:05 作者: SEED 時間: 2025-3-23 04:44 作者: Hot-Flash 時間: 2025-3-23 06:31 作者: 臨時抱佛腳 時間: 2025-3-23 10:42 作者: 等級的上升 時間: 2025-3-23 16:59 作者: depreciate 時間: 2025-3-23 19:36 作者: Density 時間: 2025-3-24 01:19
Markov Chain Monte Carlo Methods,orithms are also general-purpose algorithms, but they also require proposal densities that may be difficult to find for high-dimensional problems. In this chapter, we illustrate the use of Markov chain Monte Carlo (MCMC) algorithms in summarizing posterior distributions.作者: 放棄 時間: 2025-3-24 05:01
Model Comparison,tation of Bayes factors in both the one-sided and two-sided settings. We then generalize to the setting where one is comparing two Bayesian models, each consisting of a choice of prior and sampling density.作者: 不可思議 時間: 2025-3-24 09:59 作者: 抗生素 時間: 2025-3-24 14:01
https://doi.org/10.1007/978-981-99-4997-7rior information in a regression model. We illustrate the use of Zellner’s class of g priors to select among a set of best regression models. We conclude by illustrating the Bayesian fitting of a survival regression model.作者: Collar 時間: 2025-3-24 18:29 作者: Trochlea 時間: 2025-3-24 19:35 作者: CHARM 時間: 2025-3-25 01:47
An Introduction to R,ple Monte Carlo study to explore the behavior of the two-sample t statistic when testing from populations that deviate from the usual assumptions. We will find these data analysis and simulation commands very helpful in Bayesian computation.作者: Retrieval 時間: 2025-3-25 07:13 作者: 中世紀 時間: 2025-3-25 11:06 作者: etiquette 時間: 2025-3-25 15:36
Introduction to Bayesian Computation,r functional form, such as a member of an exponential family, and a conjugate prior is chosen for the parameter, then the posterior distribution often is expressible in terms of familiar probability distributions.作者: FRAX-tool 時間: 2025-3-25 18:07
Hierarchical Modeling,odeling. Then we consider the simultaneous estimation of the true mortality rates from heart transplants for a large number of hospitals. Some of the individual estimated mortality rates are based on limited data, and it may be desirable to combine the individual rates in some way to obtain more accurate estimates.作者: Banquet 時間: 2025-3-25 21:36 作者: exquisite 時間: 2025-3-26 01:37
Bayesian Computation with R978-0-387-92298-0Series ISSN 2197-5736 Series E-ISSN 2197-5744 作者: Arteriography 時間: 2025-3-26 05:33
A Brief Review of Immigration from Asia,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 family.作者: 十字架 時間: 2025-3-26 12:17
https://doi.org/10.1007/978-981-99-4997-7r functional form, such as a member of an exponential family, and a conjugate prior is chosen for the parameter, then the posterior distribution often is expressible in terms of familiar probability distributions.作者: 辭職 時間: 2025-3-26 16:12
https://doi.org/10.1007/978-981-99-4997-7odeling. Then we consider the simultaneous estimation of the true mortality rates from heart transplants for a large number of hospitals. Some of the individual estimated mortality rates are based on limited data, and it may be desirable to combine the individual rates in some way to obtain more accurate estimates.作者: Lymphocyte 時間: 2025-3-26 17:21
https://doi.org/10.1007/978-981-99-4997-7ppose that we partition the parameter vector of interest into . components ., where . may consist of a vector of parameters. The MCMC algorithm is implemented by sampling in turn from the . conditional posterior distributions.作者: 走調(diào) 時間: 2025-3-26 21:59
https://doi.org/10.1007/978-981-99-4997-7In this chapter, we describe the use of R to summarize Bayesian models with several unknown parameters. In learning about parameters of a normal population or multinomial parameters, posterior inference is accomplished by simulating from distributions of standard forms.作者: conduct 時間: 2025-3-27 05:05 作者: cloture 時間: 2025-3-27 07:26
Multiparameter Models,In this chapter, we describe the use of R to summarize Bayesian models with several unknown parameters. In learning about parameters of a normal population or multinomial parameters, posterior inference is accomplished by simulating from distributions of standard forms.作者: Adulterate 時間: 2025-3-27 13:01 作者: 受傷 時間: 2025-3-27 16:44 作者: 期滿 時間: 2025-3-27 19:49
978-0-387-92297-3Springer-Verlag New York 2009作者: 新手 時間: 2025-3-27 23:26 作者: Lyme-disease 時間: 2025-3-28 05:52
Use R!http://image.papertrans.cn/b/image/181832.jpg作者: Munificent 時間: 2025-3-28 08:30 作者: Collected 時間: 2025-3-28 12:50
The Immigrant Human Capital Investment Modelrtion. 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作者: Esalate 時間: 2025-3-28 18:16
A Brief Review of Immigration from Asia,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作者: 笨拙的你 時間: 2025-3-28 22:03 作者: Abutment 時間: 2025-3-29 00:09 作者: Cleave 時間: 2025-3-29 05:00
https://doi.org/10.1007/978-981-99-4997-7odeling. Then we consider the simultaneous estimation of the true mortality rates from heart transplants for a large number of hospitals. Some of the individual estimated mortality rates are based on limited data, and it may be desirable to combine the individual rates in some way to obtain more acc作者: Obloquy 時間: 2025-3-29 09:02
https://doi.org/10.1007/978-981-99-4997-7ere one is comparing two hypotheses about a parameter. In the setting where one is testing hypotheses about a population mean, we illustrate the computation of Bayes factors in both the one-sided and two-sided settings. We then generalize to the setting where one is comparing two Bayesian models, ea作者: 收集 時間: 2025-3-29 12:37 作者: crease 時間: 2025-3-29 18:02 作者: 香料 時間: 2025-3-29 21:51 作者: corpus-callosum 時間: 2025-3-30 00:40
Textbook 2009Latest editionr distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it
