ETC5410 · Bayesian inference and data analysis

Overview

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.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. appreciate the importance of Bayesian statistical techniques and understand the differences between the Bayesian and frequentist statistical paradigms
2. acquire the skills necessary to derive Bayesian results analytically, in simple models
3. demonstrate an understanding of simulation methods and be able to implement these methods in empirically realistic models for data analysis
4. understand the Kalman filter and its role in Bayesian inference in state space models.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.