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:||100:0|
Semesters and Locations
|Occurrence Code||When taught||Where taught|
|18A (HAM)||A Semester : 26 Feb 2018 - 24 Jun 2018||Hamilton|
Timetabled Lectures for Computational Statistics (STAT521)
|Tue||4:00 PM||5:00 PM||G.3.33||Feb 26 - Jun 3|
|Thu||3:00 PM||5:00 PM||G.3.33||Feb 26 - Jun 3|
|Fri||4:00 PM||5:00 PM||G.3.33||Feb 26 - Jun 3|
NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for STAT521 for more details
Indicative Fees for Computational Statistics (STAT521)
Paper Outlines for Computational Statistics (STAT521)
The following paper outlines are available for Computational Statistics (STAT521).
If your paper occurrence is not listed contact the Faculty or School office.
Other available years: Computational Statistics - STAT521 (2017)
Paper details current as of : 21 May 2018 4:26pm
Indicative fees current as of : 5 June 2018 4:30am