MTH4089 Computational statistical inference
Faculty of Science
MTH4089 Computational statistical inference is a level 4, 6-credit-point, postgraduate unit from the Faculty of Science, offered in 2020 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 6
- Offered in 2020
- Semester 1
- Clayton
This is the 2020 handbook entry. See the 2027 entry.
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Requisites
Before MTH4089
No prerequisites or corequisites besides the enrolment rules below.
After MTH4089
No unit lists MTH4089 as a prerequisite in the 2020 handbook.
Equivalent units
The same content under another code. Only one of them counts.
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 of computational statistical inference and introduce advanced computational methods used in this emerging field.
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 in 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester (Fully flex) | Clayton | Flexible |
Learning outcomes
When you finish this unit, you should be able to:
- 1
Apply sophisticated computational statistical inference in a wide range of application problems that require the integration of mathematical modelling with observed data to provide credible interpretation of the underlying system.
- 2
Explain the roles of likelihood models, missing data, and Bayesian inference and formalise parameter estimation problems in complex applications using these concepts.
- 3
Develop and apply advanced expectation-maximization methods to missing data problems.
- 4
Use the principle of Bayesian inference and apply expert computational methods to estimate parameters of statistical models and mathematical models.
- 5
Implement advanced computational methods used in statistical inference, including importance sampling, filtering, and Markov chain Monte Carlo, and understand the asymptotic behaviour of these methods.
- 6
Apply machine learning tools such as classification, Gaussian processes, and kernel methods to analyse and interpret complicate data sets and understand the computational aspects of these tools.
Workload and teaching
- 3 hours of lectures and 1 hour of tutorial per week
- 8 hours independent study per week
Contacts
- Chief Examiners
- Associate Professor Jonathan Keith
- Unit Coordinators
- Associate Professor Jonathan Keith
Common questions
What are the prerequisites for MTH4089?
MTH4089 has no prerequisites, but enrolment rules apply.
When is MTH4089 offered?
In 2020, MTH4089 runs in Semester 1 at Clayton.