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Statistics Seminar 6: Bayesian Modelling & Analysis

Files Included in this Resource:

Video recording of the speakers presentation


Resource Description:

Bayesian approaches to statistics recognise that, despite the investigators'
intention to be completely objective, their prior knowledge will inevitably affect the analysis. So Bayesian approaches explicitly include such knowledge so that its effect may be examined through sensitivity analysis. Perhaps more importantly in practical terms Markov Chain Monte Carlo (MCMC) methods enable one to analyse data using a much wider range of distributions to model the data generation process: one is not tied to normality or any of the other analytically tractable distributions used in other approaches. The session covered in this talk looked at a little of the theory, but mainly focuses on two applications which demonstrate the power of the approach. 


Further Details:

Powerpoint presentation (Powerpoint Presentation) accompanying this talk.

This talk was given by Professor Simon French, Director of RISCU

Date: 25/04/2013

More Resources in this Series

Previous- Seminar 5: Exploratory Data analysis & Multivariate Strategies

Next- Seminar 7: Regression Modelling, Variable Selection and GLMs

Further References

For further information on the International Society for Bayesian Analysis please visit:

BUGS software - Bayesian inference Using Gibbs Sampling:

–Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS -- a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325−337

–Lunn, D. J., Jackson, C., Best, N., Thomas, A. and Spiegelhalter, D. (2013). The BUGS Book: a Practical Introduction to Bayesian Analysis. London, Chapman and Hall.


W.M. Bolstad (2007). Introduction to Bayesian Statistics. 2nd Edn, Hoboken, NJ, John Wiley and Sons.

P. M. Lee (2012). Bayesian Statistics: An Introduction. 4th Edn, Chichester, John Wiley and Sons.

R. Christensen, W. Johnson, A. Branscum and T.E. Hanson (2011) Bayesian Ideas and Data Analysis. Boca Raton, CRC/Chapman and Hall

P. Congdon (2001) Bayesian Statistical Modelling. Chichester, John Wiley and Sons

S. French and D. Rios Insua (2000). Statistical Decision Theory. London, Arnold.

A. O'Hagan and J. Forester (2004). Bayesian Statistics. London, Edward Arnold.

J.M. Bernardo and A.F.M. Smith (1994). Bayesian Theory. Chichester, John Wiley and Sons.

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