Doing Bayesian Data Analysis: A Tutorial with R and BUGS


  • John Barry


Bayesian reasoning is a blessed relief to those who have always struggled with the idea that the probability of heads coming up in a supposedly fair coin flip is always 50%, even after a long series of coin flips has come up tails each time. According to Bayes, if a coin keeps coming up tails we should adjust our prior belief that the probability is 50% in the light of the posterior belief that the coin appears to be biased towards tails. John Kruschke’s book is a 600 page development of this Bayesian theme. The 23 chapters cover the basics of parameters, probability, Baye’s rule, the R and BUGS statistical programmes, the fundamentals applied to inferring a binomial proportion, and how all of this is applied to the generalized linear model. Kruschke has the rare ability amongst statistical textbook authors of writing very engagingly about knotty topics. For those who want to do what the title of the book suggests – learning to do Bayesian data analysis by learning programs languages R and BUGS – this book must be ideal.