Bayesian inference can be performed on random samples from the posterior distribution, even when it is known only in the proportional form. A sample from a candidate distribution can be reshaped, or the sample can be drawn from a Markov chain that has the posterior as its long-run distribution (MCMC). In this paper, the emphasis is on the computer implementation of these methods.
|Prerequisite(s):||Prerequisite papers: STAT221 or STAT226|
|Internal assessment / examination:||50:50|
Semesters and Locations
|Occurrence Code||When taught||Where taught|
|18B (HAM)||B Semester : 9 Jul 2018 - 4 Nov 2018||Hamilton|
Timetabled Lectures for Computational Bayesian Statistics (STAT326)
|Tue||2:00 PM||4:00 PM||G.3.33||Jul 9 - Oct 14|
|Thu||2:00 PM||4:00 PM||G.3.33||Jul 9 - Oct 14|
NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for STAT326 for more details
Indicative Fees for Computational Bayesian Statistics (STAT326)
Paper Outlines for Computational Bayesian Statistics (STAT326)
The following paper outlines are available for Computational Bayesian Statistics (STAT326).
If your paper occurrence is not listed contact the Faculty or School office.
Other available years: Computational Bayesian Statistics - STAT326 (2017)
Paper details current as of : 19 July 2018 11:44am
Indicative fees current as of : 5 June 2018 4:30am