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Activity Details

CE_21C Mon, 7/31/2017, 1:00 PM - 5:00 PM H-Key Ballroom 8
Bayesian Analysis of Big and High-Dimensional Data (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Bayesian Statistical Science
This course will provide a practical overview of state-of-the-art approaches for analyzing massive data sets using Bayesian statistical methods. The first focus area will be on algorithms for very large sample size data, and the second focus area will be on approaches for very high-dimensional data. An over-arching emphasis will be on conceptually and practical simple approaches for scaling up commonly used Markov chain Monte Carlo (MCMC) algorithms for posterior computation to be much faster to implement for huge data sets while maintaining accuracy guarantees. Some classes of algorithms include embarrassingly parallel (EP) MCMC, approximate MCMC, stochastic approximation, hybrid optimization and sampling, and modularization. Applications to computational advertising, genomics, neurosciences and other areas will provide a concrete motivation. Code and notes will be made available, and research problems of ongoing interest highlighted.
Instructor(s): David Dunson, Duke University
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