標(biāo)題: Titlebook: Bayesian Nonparametric Data Analysis; Peter Müller,Fernando Andres Quintana,Tim Hanson Book 2015 Springer International Publishing Switzer [打印本頁] 作者: 極大 時(shí)間: 2025-3-21 16:23
書目名稱Bayesian Nonparametric Data Analysis影響因子(影響力)
書目名稱Bayesian Nonparametric Data Analysis影響因子(影響力)學(xué)科排名
書目名稱Bayesian Nonparametric Data Analysis網(wǎng)絡(luò)公開度
書目名稱Bayesian Nonparametric Data Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bayesian Nonparametric Data Analysis被引頻次
書目名稱Bayesian Nonparametric Data Analysis被引頻次學(xué)科排名
書目名稱Bayesian Nonparametric Data Analysis年度引用
書目名稱Bayesian Nonparametric Data Analysis年度引用學(xué)科排名
書目名稱Bayesian Nonparametric Data Analysis讀者反饋
書目名稱Bayesian Nonparametric Data Analysis讀者反饋學(xué)科排名
作者: Fierce 時(shí)間: 2025-3-21 20:30
Human Freedom and the Logic of Eviloral data, model validation and causal inference. These themes are introduced to show by example the nature of the many application areas of nonparametric Bayesian inference that we did not include in earlier chapter.作者: 監(jiān)禁 時(shí)間: 2025-3-22 04:24 作者: avenge 時(shí)間: 2025-3-22 05:11 作者: Vasodilation 時(shí)間: 2025-3-22 10:16 作者: 行為 時(shí)間: 2025-3-22 15:50
Domènec Melé,César González Cantóncomes particularly interesting in the presence of covariates, when non- and semi-parametric Bayesian models can generalize the link function in a generalized linear model setup, the regression on covariates or both. An important application arises in inference for diagnostic screening and related in作者: 弓箭 時(shí)間: 2025-3-22 20:13
Feelings, Emotions, and Aesthetic Experienceical applications, it is natural to focus on inference for detailed features of the survival function rather than only summaries like mean and variance. We extensively discuss semi- and nonparametric Bayesian methods for survival regression. Inference for such data has been traditionally dominated b作者: Vulnerable 時(shí)間: 2025-3-22 22:47
On the Use of Scientific Argumentsre the main inference targets for many recently published applications of nonparametric Bayesian discrete mixture models. In this chapter we systematically consider the use of nonparametric Bayesian priors for inference on such random partitions. Many scientific inference problems are formalized as 作者: 高談闊論 時(shí)間: 2025-3-23 02:57
Human Freedom and the Logic of Eviloral data, model validation and causal inference. These themes are introduced to show by example the nature of the many application areas of nonparametric Bayesian inference that we did not include in earlier chapter.作者: drusen 時(shí)間: 2025-3-23 07:18
Other Inference Problems and Conclusion,oral data, model validation and causal inference. These themes are introduced to show by example the nature of the many application areas of nonparametric Bayesian inference that we did not include in earlier chapter.作者: Cacophonous 時(shí)間: 2025-3-23 10:49 作者: 挫敗 時(shí)間: 2025-3-23 16:52 作者: 范圍廣 時(shí)間: 2025-3-23 19:07
Peter Müller,Fernando Andres Quintana,Tim HansonThis is the first text to introduce nonparametric Bayesian inference from a data analysis perspective.Includes a large number of examples to illustrate the application of nonparametric Bayesian models作者: PRO 時(shí)間: 2025-3-24 00:06 作者: isotope 時(shí)間: 2025-3-24 03:56
0172-7397 zed examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages..978-3-319-36842-9978-3-319-18968-0Series ISSN 0172-7397 Series E-ISSN 2197-568X 作者: 誘惑 時(shí)間: 2025-3-24 08:04 作者: Rheumatologist 時(shí)間: 2025-3-24 10:56 作者: GONG 時(shí)間: 2025-3-24 18:07 作者: 謙虛的人 時(shí)間: 2025-3-24 19:33 作者: ANNUL 時(shí)間: 2025-3-25 01:05 作者: 調(diào)整校對(duì) 時(shí)間: 2025-3-25 05:04 作者: locus-ceruleus 時(shí)間: 2025-3-25 07:50
Other Inference Problems and Conclusion,oral data, model validation and causal inference. These themes are introduced to show by example the nature of the many application areas of nonparametric Bayesian inference that we did not include in earlier chapter.作者: 范例 時(shí)間: 2025-3-25 13:38
Survival Analysis,y the proportional hazards model. We review in detail nonparametric Bayesian alternatives which we introduce as natural generalizations of a parametric accelerated failure time model. We conclude with a discussion of three case studies.作者: Deadpan 時(shí)間: 2025-3-25 18:53 作者: 咒語 時(shí)間: 2025-3-25 22:12 作者: Vulvodynia 時(shí)間: 2025-3-26 00:55 作者: ingenue 時(shí)間: 2025-3-26 04:52
On the Use of Scientific Argumentsthe related, more general problem of feature allocation. That is, inference on possibly overlapping random subsets of experimental units. We introduce some examples from data analysis for bioinformatics data and introduce the Polya urn model, product partition models, model based clustering and the Indian buffet process prior.作者: insert 時(shí)間: 2025-3-26 10:52 作者: 性上癮 時(shí)間: 2025-3-26 13:27
Regression,shape of the response distribution is allowed to change as a function of the predictors, which is also known as density regression. We introduce the popular dependent Dirichlet process model and several other alternatives.作者: FLEET 時(shí)間: 2025-3-26 18:33
Book 2015edic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. .The discussed me作者: 包租車船 時(shí)間: 2025-3-26 23:35
0172-7397 illustrate the application of nonparametric Bayesian modelsThis book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspectiv作者: objection 時(shí)間: 2025-3-27 04:23 作者: 宏偉 時(shí)間: 2025-3-27 05:19
Human Foundations of Management its variations for density estimation. We define the model, introduce computation efficient methods for posterior inference and identify relative advantages and limitations compared with Dirichlet process models.作者: 我不死扛 時(shí)間: 2025-3-27 12:20
Domènec Melé,César González Cantónralized linear model setup, the regression on covariates or both. An important application arises in inference for diagnostic screening and related inference for ROC (receiver-operator characteristic) curves. We include some discussion of a rapidly growing literature on non-parametric Bayesian inference for ROC curves.作者: ABYSS 時(shí)間: 2025-3-27 16:11 作者: 溫順 時(shí)間: 2025-3-27 20:18
Density Estimation: Models Beyond the DP, its variations for density estimation. We define the model, introduce computation efficient methods for posterior inference and identify relative advantages and limitations compared with Dirichlet process models.作者: Magisterial 時(shí)間: 2025-3-28 00:19
Categorical Data,ralized linear model setup, the regression on covariates or both. An important application arises in inference for diagnostic screening and related inference for ROC (receiver-operator characteristic) curves. We include some discussion of a rapidly growing literature on non-parametric Bayesian inference for ROC curves.
