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Bayesian Methods

Date: 1st - 2nd March 2012
Duration: 2 days
Delivered by: Dr David Lucy

Registration deadline has passed.
Please contact psc@lancaster.ac.uk for more information about this course.

Cost
The course fees include all supporting documentation and refreshments.
  • External from industry/commerce - £510
  • External from academic institution/public sector/charity - £440
  • External postgraduate student - £300
  • Lancaster University staff - £120
  • Lancaster University postgraduate student - £60
  • Members of Mathematics and Statistics at Lancaster University - £ 0
Course description

This module introduces students to the use of Bayesian methods for data analysis in the social and empirical sciences. It also provides an introduction to the basic concepts of Bayesian approaches to statistics, ideas such as the subjective interpretation of probability, types of prior distributions, the use of Bayes’ theorem in updating information, and inference procedures such as Bayesian parameter estimates will be introduced to the student. The main focus of the module will be the application of Bayesian models in social sciences and related disciplines.

Topics covered
  • introduction to Bayesian analysis
  • single parameter Bayesian modelling
  • informative priors
  • non-informative priors
  • posterior and predictive distributions
  • conjugate distributions
  • Bayesian forms of confidence intervals
  • Bayesian regression and General Linear Models using MCMC methods and OpenBUGS
Learning
Students will acquire knowledge of:
  • the fundamental notion of Bayes' theorem and the theory of inverse probability
  • the relationship between Bayesian methods and classical likelihood methods
  • the use of Bayesian methods to combine prior information with data
  • the basic concepts of Bayesian inference, including posterior conditioning, credible intervals, prior distributions, and the likelihood principle
  • an introduction to Monte Carlo Markov Chain (MCMC) methods
  • application of MCMC using OpenBUGS to real estimation problems
  • developing skills to (a) apply theoretical concepts; (b) examine model fitting in practice using Bayesian principles; (c) explore applied Bayesian modelling
Cancellation Policy
Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.