STAT521

Computational Statistics

This paper covers maximum likelihood estimation, and the fitting of advanced regression models including non-linear models, mixture models and their generalisations. It will take a practical approach stressing the use of R packages and WinBugs or OpenBugs Bayesian software.

STAT521 - 18A (HAM)

30.0 Points

This paper covers maximum likelihood estimation, and the fitting of

advanced regression models including non-linear models, mixture models and their generalisations. It will take a practical approach stressing the use of R packages and WinBugs or OpenBugs Bayesian software.

Prerequisite(s): STAT321, or three other 300 level Statistics papers, and at the discretion of the Chairperson of Department

Internal assessment/examination ratio: 1:0

Timetabled Lectures

The Timetable for 2018 is not available.

Indicative Fees

Fees for 2018 are not yet available.

Paper Outlines

The following 2017 paper outlines are available for STAT521. Please contact the Faculty or School office for details on 2018 outlines.

Available Subjects:  Statistics | Data Analytics

Other available years: Computational Statistics - STAT521 (2017)

Paper details current as of : 20 September 2017 4:40pm
Indicative fees current as of : 21 September 2017 4:30am

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