Titlebook: Likelihood and Bayesian Inference; With Applications in Leonhard Held,Daniel Sabanés Bové Textbook 2020Latest edition Springer-Verlag GmbH

protocol

书目名称Likelihood and Bayesian Inference影响因子(影响力)




书目名称Likelihood and Bayesian Inference影响因子(影响力)学科排名




书目名称Likelihood and Bayesian Inference网络公开度




书目名称Likelihood and Bayesian Inference网络公开度学科排名




书目名称Likelihood and Bayesian Inference被引频次




书目名称Likelihood and Bayesian Inference被引频次学科排名




书目名称Likelihood and Bayesian Inference年度引用




书目名称Likelihood and Bayesian Inference年度引用学科排名




书目名称Likelihood and Bayesian Inference读者反馈




书目名称Likelihood and Bayesian Inference读者反馈学科排名

ADORE

主讲人

Textbook 2020Latest editionimportance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wal

花束

1431-8776 medicine and epidemiology with programming examples in the This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of

abject

Likelihood,tion, and Fisher information. Computational algorithms are treated to compute the maximum likelihood estimate, such as optimisation and the EM algorithm. The concept of sufficiency and the likelihood principle are finally discussed in some detail. Exercises are given at the end.

Visual-Acuity

Frequentist Properties of the Likelihood,the corresponding confidence intervals are introduced. Variance-stabilising 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.

Tracheotomy

使尴尬

Model Selection,its 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.

竞选运动

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

杀人

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.