Bayesian inference and data analysis

This unit introduces you to both foundational and methodological aspects of Bayesian inference and data analysis. Topics covered include a review of the philosophical and probabilistic foundations of Bayesian inference; the contrast between the Bayesian and frequentist (or classical) statistical paradigms; the use of prior information via the specification of objective, Jeffreys and subjective prior distributions; Bayesian linear regression; the use of simulation techniques in Bayesian inference, including Markov chain Monte Carlo algorithms; Bayesian analysis of Gaussian and non-Gaussian time series econometric models, including state space models; and the Kalman filter as a Bayesian updating rule.