• 2016 Nov 29

# STAT 300: Natesh Pillai

12:00pm to 1:00pm

## Location:

Science Center Rm. 705

Accelerating MCMC algorithms for computer models

• 2016 Nov 28

# Colloq: Kristian Lum

4:15pm to 5:15pm

## Location:

Science Center Rm. 300H

Bias in, bias out: predictive models in the criminal justice system

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 used for...

• 2016 Nov 23

# Thanksgiving Recess

Wed Nov 23 (All day) to Sun Nov 27 (All day)

• 2016 Nov 22

# STAT 310: Kaisey Mandel

1:00pm to 2:30pm

## Location:

Science Center Rm. 706

The Type Ia Supernova Color­-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

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 acceleration....

• 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...

• 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 problem....

• 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 procedures...

• 2016 Nov 11

(All day)

• 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!