Chapters
 Principle of probability
 Classical statistics vs Bayesian
 Likelihood, prior and posterior
 Bayes theorem: meaning and application
 Bayesian inference
 MCMC
 Hierarchical models
 Bayesian model selection: theory
 Bayesian model selection vs hypothesis testingensing basics
Abstract
These
lectures will give a short introduction to the fundamentals of Bayesian
inference and its application to simple but representative problems.
Numerical methods such as Markov Chain Monte Carlo will be introduced
and their practical workings illustrated. The Bayesian approach to
model selection via Bayesian model comparison will be explained and
contrasted with Frequentist hypothesis testing. 
Bibliography
 Theory and philosophy of Bayesian vs Frequentist framework:
 "Bayes in the sky: Bayesian inference and model selection in cosmology"
R. Trotta (2008) Invited review, Contemporary Physics, 49, No. 2,
MarchApril 2008, 71104 http://arxiv.org/abs/0803.4089 Section 2
 G. Cowan, Physics Today, 82, 2007 http://www.pp.rhul.ac.uk/~cowan/stat/GDCPhysicsToday.pdf
 "Why isn't every physicist a Bayesian?"
Robert
D. Cousins, (UCLA) . UCLAHEP94005, Sep 1994. 27pp. Published in
Am.J.Phys.63:398,1995.
http://ajp.aapt.org/resource/1/ajpias/v63/i5/p398_s1
 Philosophy
and practice of Bayesian statistics: Gelman & Shalizi, 2011
(unpublished)
http://www.stat.columbia.edu/~gelman/research/unpublished/philosophy.pdf
 Likelihood etc:
 "Bayesian logical data analysis for the physical sciences"
P. Gregory, CUP (2003)
 Probability and Measurement Uncertainty in Physics  a Bayesian Primer
G. D’Agostini,(1995), hepph/9512295 available here: http://arxiv.org/abs/hepph/9512295
 Bayesian methods in astronomy:
 Further reading:
