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)
“ The first talk was very well prepared and delivered. It was accessible to most newbies, but I don't think M-H and Gibbs could be understood from the talk. There was some slight imprecision (acceptance-rejection was referred to "importance sampling": the former is a method to generate random variates, and the latter is a simulation scheme). Variational methods were new to most people, and the application was very very interesting. I would have preferred a talk less devoted to the basics, and more to i) advanced methods, and ii) their R implementation.
I had to leave in the middle of the second talk. Engaging speaker. Very elementary, but easy to follow.
Overall, a very well organized event. Thanks to Joshua and Mark! ”
Talk about this Meetup
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