標(biāo)題: Titlebook: Bayesian Computation with R; Jim Albert Textbook 20071st edition Springer-Verlag New York 2007 Bayesian Inference.Hierarchical modeling.Ma [打印本頁(yè)] 作者: 推翻 時(shí)間: 2025-3-21 17:28
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(xué)科排名
書目名稱Bayesian Computation with R網(wǎng)絡(luò)公開度
書目名稱Bayesian Computation with R網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名
作者: 貨物 時(shí)間: 2025-3-21 20:53 作者: Recessive 時(shí)間: 2025-3-22 00:28
Frank Haupenthal MBA,Maximilian Gontardhe computation of the posterior distribution. One summarizes this probability distribution to perform inferences. Also one may be interested in predicting the likely outcomes of a new sample taken from the population.作者: FAST 時(shí)間: 2025-3-22 08:01 作者: palette 時(shí)間: 2025-3-22 11:28 作者: Intact 時(shí)間: 2025-3-22 13:50
Architektur des Human Capital Managements,rate estimates. We describe a two-stage model, a mixture of gamma distributions, to represent prior beliefs that the true mortality rates are exchangeable. We describe the use of R to simulate from the posterior distribution. We first use contour graphs and simulation to learn about the posterior distribution of the hyperparameters.作者: Charade 時(shí)間: 2025-3-22 18:57 作者: FATAL 時(shí)間: 2025-3-23 00:11
2197-5736 he development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. 作者: Enteropathic 時(shí)間: 2025-3-23 04:06 作者: CULP 時(shí)間: 2025-3-23 09:01 作者: Dendritic-Cells 時(shí)間: 2025-3-23 12:58
Rolf Irion,Fabian Schmidt-Schr?derd in the discrete state space situation in Section 6.2. Through a simple random walk example, we illustrate some of the important properties of a special Markov chain, and we use R to simulate from the chain and move toward the stationary distribution.作者: 叢林 時(shí)間: 2025-3-23 14:17 作者: 沙草紙 時(shí)間: 2025-3-23 18:21 作者: 產(chǎn)生 時(shí)間: 2025-3-23 23:03
Introduction to Bayesian Thinking,he computation of the posterior distribution. One summarizes this probability distribution to perform inferences. Also one may be interested in predicting the likely outcomes of a new sample taken from the population.作者: Callus 時(shí)間: 2025-3-24 04:07 作者: 元音 時(shí)間: 2025-3-24 08:14 作者: 無(wú)脊椎 時(shí)間: 2025-3-24 12:48
Hierarchical Modeling,rate estimates. We describe a two-stage model, a mixture of gamma distributions, to represent prior beliefs that the true mortality rates are exchangeable. We describe the use of R to simulate from the posterior distribution. We first use contour graphs and simulation to learn about the posterior distribution of the hyperparameters.作者: 斜坡 時(shí)間: 2025-3-24 16:21
Regression Models,or distributions of Bayesian residuals. We then illustrate the R Bayesian computations in an example where one is interested in explaining the variation of extinction times of birds in terms of their nesting behavior, their size, and their migrant status.We conclude by illustrating the Bayesian fitting of a survival regression model.作者: Gingivitis 時(shí)間: 2025-3-24 22:03
Frank Haupenthal MBA,Maximilian Gontardsimply computes values of the posterior on a grid of points and then approximates the continuous posterior by a discrete posterior that is concentrated on the values of the grid. This brute-force method can be generally applied for oneand two-parameter problems such as those illustrated in Chapters 3 and 4.作者: 傳染 時(shí)間: 2025-3-25 01:26 作者: Patrimony 時(shí)間: 2025-3-25 04:05
Introduction to Bayesian Computation,simply computes values of the posterior on a grid of points and then approximates the continuous posterior by a discrete posterior that is concentrated on the values of the grid. This brute-force method can be generally applied for oneand two-parameter problems such as those illustrated in Chapters 3 and 4.作者: 舊病復(fù)發(fā) 時(shí)間: 2025-3-25 11:12
Model Comparison,with a “streaky” model where the probability of a success may change over a season. In the second application, we illustrate the computation of Bayes factors against independence in a two-way contingency table.作者: JAMB 時(shí)間: 2025-3-25 12:21 作者: 落葉劑 時(shí)間: 2025-3-25 15:50
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 t作者: Antarctic 時(shí)間: 2025-3-25 23:17 作者: FADE 時(shí)間: 2025-3-26 01:12 作者: 堅(jiān)毅 時(shí)間: 2025-3-26 07:02 作者: 聽覺 時(shí)間: 2025-3-26 09:44 作者: 歸功于 時(shí)間: 2025-3-26 13:06 作者: tendinitis 時(shí)間: 2025-3-26 19:06 作者: 痛得哭了 時(shí)間: 2025-3-26 22:56
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作者: 的染料 時(shí)間: 2025-3-27 04:22
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.作者: poliosis 時(shí)間: 2025-3-27 07:26 作者: LAIR 時(shí)間: 2025-3-27 10:05 作者: 贊美者 時(shí)間: 2025-3-27 16:55
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作者: 安慰 時(shí)間: 2025-3-27 18:51
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作者: insurgent 時(shí)間: 2025-3-28 00:23 作者: 誤傳 時(shí)間: 2025-3-28 06:10
Rolf Irion,Fabian Schmidt-Schr?derrior 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: 倔強(qiáng)一點(diǎn) 時(shí)間: 2025-3-28 08:17 作者: 減少 時(shí)間: 2025-3-28 12:26 作者: floodgate 時(shí)間: 2025-3-28 16:16
Prozesse im Human Capital Management,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作者: 不遵守 時(shí)間: 2025-3-28 21:08 作者: right-atrium 時(shí)間: 2025-3-29 02:48 作者: 燒瓶 時(shí)間: 2025-3-29 03:55
Using R to Interface with WinBUGS,The BUGS project is focused on the development of software to facilitate Bayesian fitting of complex statistical models using Markov chain Monte Carlo algorithms. In this chapter, we introduce the use of R in running WinBUGS, a stand-alone software program for the Windows operating system.作者: 骯臟 時(shí)間: 2025-3-29 09:38 作者: Anticoagulants 時(shí)間: 2025-3-29 14:58 作者: recession 時(shí)間: 2025-3-29 16:48 作者: 紅腫 時(shí)間: 2025-3-29 21:35
Jim AlbertIntroduces Bayesian modeling by use of computation using the R language.Includes supplementary material:
