Bayesian Methods + MCMC
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Details
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)
