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

S1-01-CLAYTON-ON-CAMPUS

Requisites

Prohibition

Prerequisite

Contacts

Chief Examiner(s)

Professor Catherine Forbes

Teaching approach

Active learning

Problem-based learning

Assessment

1 - Within semester assessment

2 - Examination

Scheduled and non-scheduled teaching activities

Workshops

Workload requirements

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.

Learning resources

Technology resources

Other unit costs

Costs are indicative and subject to change.
Electronics, calculators and tools: $100