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Details available from the Department of Mathematics and Statistics.
Bayesian methods provide an approach to statistics that a rapidly-growing number of scientists are starting to use. The key difference between Bayesian and classical statistics is that Bayesian inference makes direct use of probability to represent all uncertainty. In this paper we examine the underpinnings of Bayesian inference and familiarise students with computer-based methods used in the Bayesian approach to statistics.
|Paper title||Topic in Advanced Statistics|
|Teaching period||Not offered in 2020|
|Domestic Tuition Fees (NZD)||$1,142.40|
|International Tuition Fees (NZD)||$4,661.93|
- Postgraduate students in Statistics
Enrolments for this paper require departmental permission. View more information about departmental permission.
- More information link
- View more information for STAT 443
- Teaching staff
- Dr Matthew Schofield and Dr Peter Dillingham
- Paper Structure
- Main topics:
- Subjective probability, belief and exchangeability
- Random variables
- Exponential family of distributions and conjugacy
- Monte Carlo approximation
- Inference under the normal model
- Gibbs sampling
- Hierarchical modelling
- Bayesian regression
- Metropolis-Hastings sampling
- Linear mixed effects models
- Latent variable methods
- Model checking
- Teaching Arrangements
- Three contact hours per week.
- Suggested texts:
- Gelman, A., Carlin, J., Stern, H., and Rubin, D.B. (2003) Bayesian Data Analysis, Second Edition
- Robert, C. P. (2007) The Bayesian Choice. Second edition
- Gelman, A. and Hill, J. (2006) Data Analysis Using Regression and Multilevel/Hierarchical Models
- Albert, J. (2009) Bayesian Computation with R
- Link, W. A. and Barker, R. J. (2010) Bayesian Inference with Ecological Applications
- Course outline
- View course outline for STAT 443
- Graduate Attributes Emphasised
- Critical thinking.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students who successfully complete the paper will demonstrate in-depth understanding of the central concepts of Bayesian inference.