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<h2>Bayesian Inference with PyMC and Stan </h2>
<em>Monday November 25th, 2013. 310 Warren Hall, Columbia University</em>
<p>Bayesian Estimation is taking the world by storm, and CDSS has a double header of interesting talks for this Monday, November 25th at 8pm. Our speakers, Kui Tang and Daniel Lee are both Columbia affiliates and experts in machine learning and statistical inference. They will offer an introduction to the fundamentals of bayesian statistics, why Bayesian approach is becoming much more popular now, and how you can use it. In addition, Kui Tang will do a case study with PyMC, a Markov chain Monte Carlo package for Python. Daniel Lee is a contributor to Stan, a Bayesian estimation project from Andrew Gelman's group using a variant of Hamiltonian Monte Carlo.</p>

<p>Bayesian networks are powerful and widely used machine learning models, and for good reasons: they are principled, efficient, and flexible. This talk introduces fundamentals of Bayesian statistics, walks through building a custom model. Model techniques using PyMC and Stan are presented. Open and suitable to all audiences.</p>

<p><a href="2013-11-25-Stan" style="bold">Stan slides</a> from Daniel Lee</p>