作者: Demonstrate 時間: 2025-3-21 23:24
Frequentist Properties of the Likelihood,the corresponding confidence intervals are introduced. Variance stabilizing transformations are also discussed. A case study comparing coverage and width of several confidence intervals for a proportion finishes this chapter, completed by a number of exercises at the end.作者: epidermis 時間: 2025-3-22 04:16
Bayesian Inference,ian point and interval estimates. Bayesian inference in multiparameter models is discussed and some results from Bayesian asymptotics are described. Finally, empirical Bayes methods are described, completed by a number of exercises at the end.作者: Fulminate 時間: 2025-3-22 06:55 作者: GORGE 時間: 2025-3-22 11:37
Numerical Methods for Bayesian Inference, provide ways to numerically compute posterior characteristics of interest. Monte Carlo methods, including Monte Carlo integration, rejection and importance sampling as well as Markov chain Monte Carlo are described. Finally, numerical computation of the marginal likelihood, necessary for Bayesian m作者: Obedient 時間: 2025-3-22 13:07
Prediction, predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: 鞭子 時間: 2025-3-22 20:37 作者: acetylcholine 時間: 2025-3-22 22:03
Textbook 20141st editionvanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective..?.A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis..作者: 金絲雀 時間: 2025-3-23 04:01
approaches.Complemented by exercises at the end of each chap.This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the cent作者: IRS 時間: 2025-3-23 07:35
James D. Abbey,V. Daniel R. Guide Jr.rtance sampling as well as Markov chain Monte Carlo are described. Finally, numerical computation of the marginal likelihood, necessary for Bayesian model selection, is discussed. Exercises are given at the end.作者: Saline 時間: 2025-3-23 12:32
Numerical Methods for Bayesian Inference,rtance sampling as well as Markov chain Monte Carlo are described. Finally, numerical computation of the marginal likelihood, necessary for Bayesian model selection, is discussed. Exercises are given at the end.作者: hematuria 時間: 2025-3-23 17:13 作者: conjunctivitis 時間: 2025-3-23 18:58 作者: ALE 時間: 2025-3-24 00:30
http://image.papertrans.cn/b/image/160151.jpg作者: 退出可食用 時間: 2025-3-24 03:21 作者: 舔食 時間: 2025-3-24 08:22
https://doi.org/10.1007/978-3-030-19970-8the corresponding confidence intervals are introduced. Variance stabilizing transformations are also discussed. A case study comparing coverage and width of several confidence intervals for a proportion finishes this chapter, completed by a number of exercises at the end.作者: 和平主義者 時間: 2025-3-24 14:03 作者: 審問,審訊 時間: 2025-3-24 18:26
https://doi.org/10.1007/978-3-319-30094-8its connection to cross-validation. Bayesian model selection based on the marginal likelihood is described, including Bayesian model averaging. Finally, DIC is introduced, completed by a number of exercises at the end.作者: 原諒 時間: 2025-3-24 21:18 作者: Iatrogenic 時間: 2025-3-24 23:29
Vishal V. Agrawal,Ioannis Bellos predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: dermatomyositis 時間: 2025-3-25 06:58 作者: 世俗 時間: 2025-3-25 07:31 作者: 啟發(fā) 時間: 2025-3-25 12:51 作者: 有斑點 時間: 2025-3-25 18:46 作者: maintenance 時間: 2025-3-25 23:00
Elements of Frequentist Inference,This chapter discusses fundamental concepts of frequentist inference, such as unbiasedness and consistency, standard errors and confidence intervals, significance tests and P-values. There is also a section on the bootstrap method. Exercises are given at the end.作者: strain 時間: 2025-3-26 03:30
Likelihood Inference in Multiparameter Models,The concepts described in Chap. . are now extended to multiparameter models. The concept of profile likelihood is introduced as well as the generalized likelihood ratio statistic. The conditional likelihood, an alternative way to eliminate a nuisance parameter, is discussed. Exercises are given at the end.作者: Ingratiate 時間: 2025-3-26 06:22 作者: 圓桶 時間: 2025-3-26 11:33 作者: Crayon 時間: 2025-3-26 13:45
Bayesian Inference,ian point and interval estimates. Bayesian inference in multiparameter models is discussed and some results from Bayesian asymptotics are described. Finally, empirical Bayes methods are described, completed by a number of exercises at the end.作者: altruism 時間: 2025-3-26 19:04 作者: Fecal-Impaction 時間: 2025-3-26 20:56
Prediction, predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: 鍵琴 時間: 2025-3-27 03:53
https://doi.org/10.1007/978-3-642-37887-4Bayesian Inference; Likelihood Inference; Model Choice; Prediction作者: Insensate 時間: 2025-3-27 05:59
Springer-Verlag Berlin Heidelberg 2014作者: jabber 時間: 2025-3-27 09:59 作者: 鍵琴 時間: 2025-3-27 16:53 作者: squander 時間: 2025-3-27 18:57 作者: 難取悅 時間: 2025-3-28 00:44 作者: 輕浮女 時間: 2025-3-28 02:21
Vishal V. Agrawal,Ioannis Bellos predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: 小隔間 時間: 2025-3-28 07:26
10樓作者: integrated 時間: 2025-3-28 11:46
10樓作者: 賄賂 時間: 2025-3-28 17:24
10樓
