• 2016 Nov 28

# Colloq: Kristian Lum

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

Predictive models are increasingly used in the criminal justice system to try to predict who will commit crime in the future and where that crime will occur. But what happens when these models are trained using biased data? In this talk, I will present two examples of how biased data is used in the criminal justice system. In the first example, I will introduce a recently published model...

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• 2016 Nov 22

# STAT 310: Kaisey Mandel

1:00pm to 2:30pm

## Location:

Science Center Rm. 706

Type Ia supernovae (SN Ia) are faraway exploding stars used as standardizable candles'' to determine cosmological distances, measure the accelerating expansion of the Universe, and constrain the properties of dark energy. Inferring peak luminosities of SN Ia from distance-independent observables, such as the shapes and colors of their light curves (time series), underpins the evidence for cosmic...

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• 2016 Nov 21

# Colloq: Kari Lock Morgan

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

Balancing Covariates via Propensity Score Weighting: The Overlap Weights

Propensity score weighting is often utilized to achieve covariate balance when comparing treatment groups in observational studies. Here we define a general class of balancing weights that balance the weighted covariate distribution between groups. This class includes the commonly used inverse-probability weights, but we illustrate here why these weights can be problematic if covariates...

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• 2016 Nov 15

# STAT 310: Kai Zhang

1:15pm to 2:30pm

## Location:

Science Center Rm. 705

BET on Independence

We study the problem of model-free dependence detection. This problem can be difficult even when the marginal distributions are known. We explain this difficulty by showing the impossibility to uniformly consistently distinguish degeneracy from independence with any single test. To make model-free dependence detection a tractable problem, we introduce the concept of binary expansion statistics (BEStat) and propose the binary expansion testing (BET) framework. Through simple mathematics, we convert the dependence detection problem to a multiple testing...

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• 2016 Nov 15

# STAT 300: Edo Airoldi

12:00pm to 1:00pm

## Location:

Science Center Rm. 705
Model-assisted design of experiments on networks
• 2016 Nov 14

# Colloq: Sarah Filippi

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

Bayesian nonparametric approaches to quantifying dependence between random variables

Nonparametric and nonlinear measures of statistical dependence between pairs of random variables have proved themselves important tools in modern data analysis, where the emergence of large data sets can support the relaxation of linearity assumptions implicit in traditional association scores such as correlation. In this talk, I will present two Bayesian nonparametric...

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• 2016 Nov 08

# STAT 300: PhD Town Hall

12:00pm to 1:00pm

## Location:

Science Center Rm. 705
Please note that this STAT 300 Town Hall is reserved for PhD students and Neil Shephard. Thanks!
• 2016 Nov 07

# Colloq: Yves Atchade

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

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...

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• 2016 Nov 01

# STAT 300: Keith Chan

12:00pm to 1:00pm

## Location:

Science Center Rm. 705
"Robust Optimal Estimation of Asymptotic Covariance Matrix in Non-stationary Multi-dimensional Time Series" by Keith Chan
• 2016 Oct 31

# Colloq: Nick Whiteley

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

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...

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