Search Course Outline
6 course outlines found
1
STATS 731
: Bayesian Inference2025 Semester Two (1255)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
2
STATS 731
: Bayesian Inference2024 Semester Two (1245)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
3
STATS 731
: Bayesian Inference2023 Semester Two (1235)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
4
STATS 731
: Bayesian Inference2022 Semester Two (1225)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225
5
STATS 731
: Bayesian Inference2021 Semester Two (1215)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225
6
STATS 731
: Bayesian Inference2020 Semester One (1203)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 210 or 225