Seminar on Bayesian Inference (full day event!)

We're pleased to announce that John Myles White will be in town on Tuesday, March 20th, for a full-day seminar (http://www.cs.gmu.edu/events/index.html#15-BayesianIn) on Bayesian Inference at the Computer Science Department of George Mason University in Fairfax, VA. The event is open to the public, and we would be very happy for DSDC Members and others to attend! There is a small charge to cover John's expenses, which is optional for students and faculty of GMU and other educational institutions.

The seminar begins at 8am and ends at 3:30pm, with a break from 11am to 12:30pm. The location is "SUB II (The Hub) Rooms 3, 4, and 5", and The Hub can be found on this map of campus (http://info.gmu.edu/Maps/FairfaxColorGridTabloid11.pdf) (pdf).

The content to be covered is as follows:

Section 1:

An Introduction to Bayesian Inference Introduce the Bayesian paradigm of inference as probabilistic calculation Provide a loose treatment of the Cox axioms Discuss useful statistical theory: Likelihood functions Maximum likelihood estimation Fisher information Bias, variance, consistency and the Central Limit Theorem for estimators Review standard probability distributions Go through the classical coin-filpping example in detail with a beta prior Describe results of Bayesian inference as comparable to MLE with regularization added in Section 2:

BUGS as a Tool for Automating Bayesian Inference Describe how to specify models using BUGS language Go through many example models Normal with unknown mean, known variance Normal with unknown mean, unknown variance Linear regression: unknown coefficients and variance, Normal priors Linear regression with Laplace priors Logistic regression Hierarchical models LDA SNA models

Bio: John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Along with the political scientist Drew Conway, he is the author of a book recently published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. John is now working with the statistician Mark Hansen on a book for laypeople about exploratory data analysis. He is also the lead maintainer for several popular R packages, including ProjectTemplate and log4r.