Computational Bayesian Statistics - STAT326 (2017)
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.
Trimesters and Locations
|Occurrence Code||When taught||Where taught|
|17B (HAM)||B Trimester : 10 Jul 2017 - 29 Oct 2017||Hamilton|
The Timetable for 2017 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: Statistics
Other available years: Computational Bayesian Statistics - STAT326 (2018)
Paper details current as of : 20 March 2020 8:55am
Indicative fees current as of : 31 January 2020 4:26pm