2016 - 2017

2017 Mar 28

STAT 300: Robin Gong

12:00pm to 1:00pm

Location: 

Science Center Rm. 705
An SMC approach to set-valued posterior inference
2016 Oct 04

STAT 300: Espen Bernton and Stephane Shao

12:00pm to 1:00pm

Location: 

Science Center Rm. 705

"Statistical inference for generative models using the Wasserstein distance," by Espen Bernton

"Model selection for state-space models," by Stephane Shao

2016 Oct 25

STAT 300: Finale Doshi-Velez

12:00pm to 1:00pm

Location: 

Science Center Rm. 705

Non-identifiability and Posterior Exploration in Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) is a popular model for data exploration: each data point can be thought of as a convex, linear combination of a set of bases, and the bases represent something important about the structure of the data.  I will first talk about non-identifiability in NMF -- which can thwart interpretation -- including some counter-intuitive examples of how factorizations may not be unique.  Next, I will describe on-going work in my group on how to combine some of...

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2016 Nov 07

Colloq: Yves Atchade

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Bayesian statistics without likelihood

Bayesian analysis of high-dimensional graphical models often leads to posterior distributions that are computationally intractable. Similar issues arise with other classes of statistical models. In this talk I will advocate the use of more general loss functions in the Bayesian machinery. The idea is not new, but I will present some new results on the contraction properties of the resulting quasi-posterior distributions...

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2016 Oct 31

Colloq: Nick Whiteley

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Log-concavity and importance sampling in high dimensions

Importance sampling is one of the elementary Monte Carlo methods for approximating expectations with respect to a 'target' distribution whose normalizing constant is unknown. The idea is simply to draw samples from some other distribution which dominates the target, then approximate the expectation of interest using appropriately weighted sample averages.

Direct application of this idea can...

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2017 Jan 31

Seminar: Rajarshi Mukherjee

12:00pm to 1:00pm

Location: 

Science Center Rm. 705

Sparse Signal Detection with Binary Outcomes

In this talk, I will discuss three examples of sparse signal detection problems in the context of binary outcomes. These will be motivated by examples from next generation sequencing association studies, understanding heterogeneities in large scale networks, and exploring opinion distributions over networks. Moreover, these three examples will serve as templates to explore interesting phase transitions present in such studies. In particular, these phase transitions will be aimed at revealing a difference between studies with...

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