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STAT443 Topic in Advanced Statistics

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
Paper code STAT443
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees (NZD) $1,076.55
International Tuition Fees (NZD) $4,267.52

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Eligibility
Postgraduate students in Statistics

Enrolments for this paper require departmental permission. View more information about departmental permission.
Contact
mparry@maths.otago.ac.nz
Teaching staff
Professor Richard Barker and Dr Matthew Schofield
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
Textbooks
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.

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Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

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
Paper code STAT443
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period First Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Eligibility
Postgraduate students in Statistics

Enrolments for this paper require departmental permission. View more information about departmental permission.
Contact
mparry@maths.otago.ac.nz
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.
Textbooks
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.

^ Top of page

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other