A 2 day course from 12th - 13th March 2018.
This module introduces students to the use of Bayesian methods for data analysis in the social and empirical sciences. It provides an introduction to conditional probability and Bayes' theorum and its application to the calculation of everyday probabilities. The discussion is then extended to the use od Bayes' theorum to calculate statistics. Ideas such as the subjective interpretation of probability, types of prior distributions, and the use of Bayes theorem in updating information. Inference procedures such as Bayesian parameter estimates will be introduced. The main focus of the module will be the application of Bayesian models in the social and environmental sciences and related disciplines.
Important please note:
If the course is fully booked please do complete the registration, as then you will be placed on a waiting list. You will be allocated a place if more places become available or if people cancel.
We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.
Details of course fees.
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.
Topics covered will be:
An introduction to Bayesian analysis, single parameter Bayesian modelling, informative priors, noninformative priors, posterior and predictive distributions, conjugate distributions, Bayesian forms of confidence intervals, Bayesian regression and General Linear Models using MCMC methods and OpenBUGS.
Exercises will be provided as part of the practical sessions. The assessment will be a short assignment on Bayesian model fitting using OpenBUGS.
Students will acquire a knowledge of:
- the fundamental notion of Bayes' theorem and the theory of inverse probability
- the relationship between Bayesian methods and classical likelihood based methods
- the use of Bayesian methods to combine prior information with data
- the basic concepts of Bayesian inference, credible intervals, prior distributions.
- an introduction to Monte Carlo Markov Chain (MCMC) methods
- application of MCMC using OpenBUGS to real estimation problems
and develop skills to:
- apply theoretical concepts
- examine model fitting in practice using Bayesian principles
- explore applied Bayesian modelling