The author in his preface says that the book is targeted not towards the 1 reader in 100 who will go on to specialize in statistical analysis, but for the other 99 who will only obtain an overview of the subject, yet will have to deal in their professional lives with the design, analysis and interpretation of research by interfacing with specialists in the field. Indeed by the end of the book, a reader can walk away with a decent intuition of the multivariate statistical techniques.

[youtube https://www.youtube.com/watch?v=HIudxeqFuz8?rel=0]

I compute, therefore I understand - Judea Pearl [youtube https://www.youtube.com/watch?v=78EmmdfOcI8?rel=0]

Nicholas Constantine Metropolis This is a great write up on the nuts and bolts of Metropolis Hastings Algorithm. I like such papers that summarize everything about an algorithm with a sound balance of rigor and simplicity. Nowadays even for a simple statistical analysis, one tends to specifying a BUGS model and run MCMC. With BUGS software, Bayes analysis has become accessible to a whole lot of data analysts. The heart of BUGS is the Gibbs sampling algorithm, which is a special case of Metropolis Hastings Algorithm.

Most of us would have come across Occam’s razor principle in the context of variable selection, the essence of which is, “parsimony wins”. However not many would have heard about “Occam’s window” that is relevant in the context of Model selection, i.e. choosing a set of models out of an ocean of potential models. In the stats literature, Occam’s window appears under Bayesian Model Selection. In this post, I will try to summarize some of the main points from this fantastic paper by Adrian Raftery.