Bayesian Methods + MCMC

Still hammering out the details, but it is likely that we will have two presenters, Jake Hofman and Suresh Velagapundi - who will discuss both the nature and and application of Bayesian methods both from a theoretical perspective and through applied examples in R.

Jake will present:

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)