Overview
Computational statistical inference merges statistics with computational mathematics stochastic computation, computational linear algebra, and optimization to fully exploit the power of ever-increasing data sets, sophisticated mathematical models, and cutting-edge computer architectures. Driven by applied problems in finance, biology, geophysics, and data analytics, this unit aims to provide an integrated view … For more content click the Read More button below.
This unit covers both practical algorithms and theoretical foundations of statistical inference, with cases studies on a selection of application problems. The main topics are parameter estimation and Bayesian inference, missing data problems and expectation maximisation, advanced Monte Carlo methods including importance sampling and Markov chain Monte Carlo, approximate Bayesian computation, linear and nonlinear filtering methods, classification, Gaussian processes, and kernel methods.
Offerings
S1-01-CLAYTON-ON-CAMPUS
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Associate Professor Jonathan Keith
Unit Coordinator(s)
Associate Professor Jonathan Keith
Assessment
1 - Continuous assessment
2 - Examination (3 hours and 10 minutes)
Scheduled and non-scheduled teaching activities
Applied sessions
Lectures
Workload requirements
Workload
Other unit costs
Costs are indicative and subject to change.
Miscellaneous items required (Printing, Stationery) - $100.
Availability in areas of study
Master of Mathematics