標(biāo)題: Titlebook: Bayesian Core: A Practical Approach to Computational Bayesian Statistics; Jean-Michel Marin,Christian P. Robert Textbook 20071st edition S [打印本頁(yè)] 作者: cessation 時(shí)間: 2025-3-21 17:57
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書目名稱Bayesian Core: A Practical Approach to Computational Bayesian Statistics讀者反饋
書目名稱Bayesian Core: A Practical Approach to Computational Bayesian Statistics讀者反饋學(xué)科排名
作者: Outshine 時(shí)間: 2025-3-21 20:53
Springer-Verlag New York 2007作者: Digest 時(shí)間: 2025-3-22 00:25 作者: 現(xiàn)任者 時(shí)間: 2025-3-22 05:31
Human Capital and Economic Growth testing assimilation of the techniques. We then propose a corresponding statistical model centered on the normal N (μ, σ.) distribution and consider specific inferential questions to address at this level, namely parameter estimation, one-sided test, prediction, and outlier detection, after we set 作者: 護(hù)航艦 時(shí)間: 2025-3-22 10:46
Giam Pietro Cipriani,Tamara Fioroniand thus to uncover explanatory and predictive patterns. This chapter unfolds the Bayesian analysis of the linear model both in terms of prior specification (conjugate, noninformative, and Zellner’s G-prior) and in terms of variable selection, the next chapter appearing as a sequel for nonlinear dep作者: recession 時(shí)間: 2025-3-22 13:20
https://doi.org/10.1057/978-1-137-56561-7a single transformation of the data that must achieve the possibly conflicting goals of normality and linearity imposed by the linear regression model, which is for instance impossible for binary or count responses. The trick that allows both a feasible processing and an extension of linear regressi作者: 胡言亂語(yǔ) 時(shí)間: 2025-3-22 19:15 作者: miracle 時(shí)間: 2025-3-22 23:21 作者: 是突襲 時(shí)間: 2025-3-23 04:59
Occupational Characteristics Analysisth series in the picture above!). As in the previous chapters, the difficulty in modeling such datasets is to balance the complexity of the representation of the dependence structure against the estimation of the corresponding model—and thus the modeling most often involves model choice or model com作者: concert 時(shí)間: 2025-3-23 07:41 作者: 一再煩擾 時(shí)間: 2025-3-23 10:45 作者: Impugn 時(shí)間: 2025-3-23 14:51 作者: athlete’s-foot 時(shí)間: 2025-3-23 19:03
Bayesian Core: A Practical Approach to Computational Bayesian Statistics978-0-387-38983-7Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: Diverticulitis 時(shí)間: 2025-3-24 01:21
https://doi.org/10.1057/978-1-137-56561-7ical side, we present a general MCMC method, the Metropolis–Hastings algorithm, which is used for the simulation of complex distributions where both regular and Gibbs sampling fail. This includes in particular the random walk Metropolis–Hastings algorithm, which acts like a plain vanilla MCMC algorithm.作者: MOTTO 時(shí)間: 2025-3-24 03:09
Svetoslav Danchev,Grigoris Pavlouo spatial statistics we will provide in this book, and we thus very briefly mention Markov random fields, which are extensions of Markov chains to the spatial domain. A complete reference on this topic is M?ller (2003).作者: Isolate 時(shí)間: 2025-3-24 08:52 作者: output 時(shí)間: 2025-3-24 11:20
Image Analysis,o spatial statistics we will provide in this book, and we thus very briefly mention Markov random fields, which are extensions of Markov chains to the spatial domain. A complete reference on this topic is M?ller (2003).作者: DUCE 時(shí)間: 2025-3-24 15:36
1431-875X yesian computing for the most classical models.ComputationalAfter that, it was down to attitude. —Ian Rankin, Black & Blue. — The purpose of this book is to provide a self-contained (we insist!) entry into practical and computational Bayesian statistics using generic examples from the most common mo作者: reception 時(shí)間: 2025-3-24 20:14
Human Capital and Economic Growththe description of the Bayesian resolution of inferential problems. This being the first chapter, the amount of technical/theoretical material may be a little overwhelming at times. It is, however, necessary to go through these preliminaries before getting to more advanced topics with a minimal number of casualties!作者: phlegm 時(shí)間: 2025-3-25 02:42
Normal Models,the description of the Bayesian resolution of inferential problems. This being the first chapter, the amount of technical/theoretical material may be a little overwhelming at times. It is, however, necessary to go through these preliminaries before getting to more advanced topics with a minimal number of casualties!作者: 畏縮 時(shí)間: 2025-3-25 03:42 作者: 使入迷 時(shí)間: 2025-3-25 11:07 作者: tariff 時(shí)間: 2025-3-25 15:37 作者: Progesterone 時(shí)間: 2025-3-25 17:13 作者: Apraxia 時(shí)間: 2025-3-25 23:28 作者: HAUNT 時(shí)間: 2025-3-26 01:02 作者: 培養(yǎng) 時(shí)間: 2025-3-26 05:58
,Capture–Recapture Experiments,the generic Arnason–Schwarz model that is customarily used for open populations..On the methodological side, we provide an entry into the accept–reject method, which is the central simulation technique behind most standard random generators and relates to the Metropolis–Hastings methodology in many ways.作者: 正常 時(shí)間: 2025-3-26 11:37 作者: 不易燃 時(shí)間: 2025-3-26 16:37
Bayesian Core: A Practical Approach to Computational Bayesian Statistics作者: 捕鯨魚叉 時(shí)間: 2025-3-26 18:27 作者: 豎琴 時(shí)間: 2025-3-26 23:41 作者: Curmudgeon 時(shí)間: 2025-3-27 01:24
Textbook 20071st editions in all ?elds, given the versatility of the Bayesian tools. It can also be used for a more classical statistics audience when aimed at teaching a quick entry to Bayesian statistics at the end of an undergraduate program for instance. (Obviously, it can supplement another textbook on data analysis a作者: 咯咯笑 時(shí)間: 2025-3-27 05:30
Svetoslav Danchev,Grigoris Pavlou concept of “variable dimension models,” where the structure (dimension) of the model is determined a posteriori using the data. This opens new perspectives for Bayesian inference such as model averaging but calls for a special simulation algorithm called reversible jump MCMC.作者: poliosis 時(shí)間: 2025-3-27 10:11 作者: 經(jīng)典 時(shí)間: 2025-3-27 16:38 作者: 旁觀者 時(shí)間: 2025-3-27 21:12 作者: MAUVE 時(shí)間: 2025-3-28 01:39 作者: 圍巾 時(shí)間: 2025-3-28 03:00 作者: Flounder 時(shí)間: 2025-3-28 09:39 作者: 津貼 時(shí)間: 2025-3-28 13:18
Mixture Models,known distributions. This representation is naturally called a mixture of distributions. Inference about the parameters of the elements of the mixtures and the weights is called mixture estimation, while recovery of the original distribution of each observation is called classification (or, more exa作者: 罐里有戒指 時(shí)間: 2025-3-28 16:03 作者: 形狀 時(shí)間: 2025-3-28 20:50 作者: GOUGE 時(shí)間: 2025-3-29 02:18
