Date: Friday, Nov 20, 2020
Time : 5:00 pm - 6:00 pm (Indian Standard Time)
Join Zoom Meeting
https://us02web.zoom.us/j/82286271984?pwd=VHVUM2pFVFExU2owaEhXU3lGZSs3UT09
Meeting ID: 822 8627 1984
Passcode: 455327
Scalable Monte Carlo algorithms for Bayesian inference
Deborshee Sen
Postdoctoral Associate, SAMSI, and Department of Statistical Science, Duke University, USA.
20-11-20

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Abstract

The Bayesian framework is an appealing technique for conducting statistical inference with accurate uncertainty quantification. Numerical methods are routinely deployed since analytical solutions are often not available. Among them, Monte Carlo algorithms are arguably the most popular approach. However, such algorithms become computationally challenging as the size of the data increases. This has motivated significant recent attention to the development and study of scalable Monte Carlo algorithms for Bayesian inference. In this talk, I will first give a background to the problem of Bayesian inference for large data. I will then discuss scalable algorithms for inference, including my own contributions to the area.