This Thursday at 7pm Jake Hofman and Suresh Velagapundi will present a session at New York R Statistical Programming Meetup at NYU – Silver Center (100 Washington Square East, Room 401). Here’s the outline:

Background:
Conditional probability & Bayes’ Rule
Treating parameters as random variables & putting distributions on them
Bayesian inference: from priors & likelihoods to posteriors

From Principles to Practice:
Simple plan; difficult to execute (normalization)
Resort to approximation methods (variational & MCMC)
Model selection / complexity control a la Bayes (time permitting)