Computational Bayesian Statistics - STAT326 (2018)
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):||STAT221, STAT226 or STATS226 or at the discretion of the Paper Convenor|
|Internal assessment / examination:||50:50|
Trimesters and Locations
|Occurrence Code||When taught||Where taught|
|18B (HAM)||B Trimester : 9 Jul 2018 - 4 Nov 2018||Hamilton|
The Timetable for 2018 is not available.
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.
Available Subjects: Data Analytics
Other available years: Computational Bayesian Statistics - STAT326 (2017)
Paper details current as of : 31 March 2020 10:22am
Indicative fees current as of : 31 January 2020 4:26pm