Understanding Computational Bayesian Statistics

Understanding Computational Bayesian Statistics

Providing a solid grounding in statistics while uniquelycovering the topics from a Bayesian perspective, UnderstandingComputational Bayesian Statistics successfully guides readersthrough this new, cutting-edge approach. With its hands-ontreatment of the topic, the book shows how samples can be drawnfrom the posterior distribution when the formula giving its shapeis all that is known, and how Bayesian inferences can be based onthese samples from the posterior. These ideas are illustrated oncommon statistical models, including the multiple linear regressionmodel, the hierarchical mean model, the logistic regression model,and the proportional hazards model.