In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Carlin and Louis - Bayes and Empirical Bayes Methods for Data Analysis Gelman, Carlin, Stern and Rubin - Bayesian Data Analysis Bernardo and Smith - Bayesian Theory Gilks, Richardson and Spiegelhalter ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 62, No. 1 (2000), pp. 57-75 (19 pages) Hidden Markov models form an extension of mixture models which provides a ...

A probability is a number that takes some value equal to or between zero and one. If the probability of the 'event' of interest is zero, then the event cannot occur. So, for example, the probability ...

Regular Bayesian and frequentist approximations in statistics are studied within a unified framework. In particular it is shown how some higher-order likelihood-based approximations arise from their ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...