Spring 2017 Seminars

Wednesday, April 19, 2017

The Secret Life of I. J. Good

Time: 11:00 a.m.

Speaker: Sandy Zabell, Professor of Statistics and Mathematics Northwestern University; Director of Undergraduate Studies, Department of Statistics

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract:  I. J. ("Jack") Good was an important Bayesian statistician for more than half a century after World War II, and played an important role in the (eventual) post-war Bayesian revival.  But his graduate training had been in mathematical analysis (one of his advisors had been G. H. Hardy);  what was responsible for this metamorphosis from pure mathematician to statistician?

As Good only revealed in 1976, during the war he had initially served as an assistant to Alan Turing at Bletchley Park, working on the cryptanalysis of the German Naval Enigma, and it was from Turing that he acquired his life-long Bayesian philosophy. Declassified documents now permit us to understand in some detail how this came about, and indeed how many of the ideas Good discussed and papers he ​wrote in the initial decades after the war in fact presented in sanitized form results that had had their origins in his wartime work.  In this talk, drawing on these newly available sources, I will discuss the daily and very real use of Bayesian methods that Turing and Good employed, and how this was very gradually revealed by Good over the course of his life (including revealing his return to classified work in the 1950s).

Wednesday, April 26, 2017

Simple, Scalable and Accurate Posterior Interval Estimation

Time: 11:00 a.m.

Speaker: Cheng Li, Assistant Professor, Department of Statistics and Applied Probability, National University of Singapore

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: Standard posterior sampling algorithms, such as Markov chain Monte Carlo, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarantees and show that the credible intervals from our algorithm asymptotically approximate those from the full posterior in the leading parametric order. Our theory also accounts for the Monte Carlo errors from posterior sampling. We compare the empirical performance of our algorithm with several competing embarrassingly parallel MCMC algorithms in both simulations and a real data example. We also discuss possible extensions to multivariate posterior credible regions.

Wednesday, May 10, 2017

Time: 11:00 a.m.

Speaker: Rina Barber, Assistant Professor, Department of Statistics, University of Chicago

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Wednesday, May 17, 2017

A Group-Specific Recommender System

Time: 11:00 a.m.

Speaker: Annie Qu, Professor and Director of Illinois Statistics Office, Department of Statistics, University of Illinois at Urbana-Champaign

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road

Abstract: In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In this talk, we propose a group-specific method to utilize dependency information from users and items which share similar characteristics under the singular value decomposition framework. The new approach is effective for the “cold-start” problem, where, in the testing set, majority responses are obtained from new users or for new items, and their preference information is not available from the training set. One advantage of the proposed model is that we are able to incorporate information from the missing mechanism and group-specific features through clustering based on the numbers of ratings from each user and other variables associated with missing patterns.  Our simulation studies and MovieLens data analysis both indicate that the proposed group-specific method improves prediction accuracy significantly compared to existing competitive recommender system approaches. In addition, we also extend the recommender system for the tensor data with multiple arrays. 

Wednesday, May 24, 2017

Time: 11:00 a.m.

Speaker: Dan Apley, Professor, Department of Industrial Engineering and Management Sciences, Northwestern University

Place: Basement classroom - B02, Department of Statistics, 2006 Sheridan Road