Titlebook: Applied Statistical Inference; Likelihood and Bayes Leonhard Held,Daniel Sabanés Bové Textbook 20141st edition Springer-Verlag Berlin Heide

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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.

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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.

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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.

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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.