The book "Bayesian analysis: general framework" deals with Bayesian inference within the framework of four stochastic models: the normal random sample, the Gauss-Markov model, the beta-binomial model and the Poisson-gamma model. For each of these models, analytic expressions are presented for the posterior PDF, the prior predictive PDF and the posterior predictive PDF, under the Laplace prior PDF, the Jeffreys prior PDF and the conjugate prior PDF. Topics such as the elicitation of hyper-parameter for the conjugate prior PDF, the selection of models and prediction in multivariate regression analysis and the use of the Bayes factor in the test of hypothesis are dealt with more detail.
The book "Bayesian analysis: general framework" deals with Bayesian inference within the framework of four stochastic models: the normal random sample, the Gauss-Markov model, the beta-binomial model and the Poisson-gamma model. For each of these models, analytic expressions are presented for the posterior PDF, the prior predictive PDF and the posterior predictive PDF, under the Laplace prior PDF, the Jeffreys prior PDF and the conjugate prior PDF. Topics such as the elicitation of hyper-parameter for the conjugate prior PDF, the selection of models and prediction in multivariate regression analysis and the use of the Bayes factor in the test of hypothesis are dealt with more detail.
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian...
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book continues to take an applied approach to analysis using up-to-date...
With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen...
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