Bayesian Methods for Statistical Analysis

• Main Author: Puza, Borek (auth)
• Format: eBook
• Published: ANU Press 2015
• Subjects: Mathematics; Probability & statistics; Bayesian inference; statistics; mathematics; bayesian inference; probability; Algorithm; Confidence interval; Histogram; Monte Carlo method; Posterior probability; Sampling (statistics); WinBUGS
• Online Access: Get fulltext

 Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.