2016 - 2017

2017 Feb 14

STAT 300: Jameson Quinn

12:00pm to 1:00pm

Location: 

Science Center Rm. 705
Using locality to beat the curse of dimension in particle filters
2016 Sep 26

Colloq: Marie-Abele Bind

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Transporting established insights from classical experimental design to address causal questions in environmental epidemiology, including the understanding of biological mediating mechanisms

There is a fundamental gap in addressing causality in observational studies due to missing data, lack of randomization, and complications due to temporality. Measures of association are not optimal for making relevant policy recommendations because these involve...

Read more about Colloq: Marie-Abele Bind
2016 Dec 13

STAT 300: Zach Branson and Maxime Rischard

12:00pm to 1:00pm

Location: 

Science Center Rm. 705

"A Nonparametric Bayesian Methodology for Regression Discontinuity Designs," by Zach Branson

TBD by Maxime Rischard

2017 Feb 07

STAT 300: Reagan Rose and Jameson Quinn

12:20pm to 1:10pm

Location: 

Science Center Rm. 705

"Guiding jurors with prior-award information: a case study in causal inference from factorial design" by Reagan Rose

"Using locality to beat the curse of dimension in particle filters" by Jameson Quinn

2017 Feb 14

STAT 310: Xufei Wang and Luis Campos

1:00pm to 2:30pm

Location: 

Science Center Rm. 706
AstroStat Talks 2016-2017 "Bounding a good region," by Xufei Wang "Separating close sources by their temporal behavior," by Luis Campos
2016 Jul 14

Stat Colloq: Fabio Cuzzolin

4:15pm to 5:15pm

Location: 

Science Center Rm. 705

Belief Functions: Past, Present, and Future

The theory of belief functions, sometimes referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, to be later developed by Glenn Shafer as a general framework for modelling epistemic uncertainty. Belief theory and the closely related random set theory form a natural framework for modelling situations in which data are missing or scarce: think of extremely rare events such as volcanic eruptions or power plant meltdowns, problems subject to huge...

Read more about Stat Colloq: Fabio Cuzzolin
2016 Dec 05

Colloq: Edward Glaeser & Nikhil Naik

4:15pm to 5:15pm

Location: 

Science Center Rm. 300H

Visualizing the City

Google Streetview now provides an enormously rich picture of the physical streetscapes of the world's cities. Advances in computer recognition techniques make it possible to use images to predict local demographics or the curb appeal of particular places. We show how the combination of Google Streetview and computer vision techniques can map the patterns of neighborhood in six American cities. We also use these methods to predict income in...

Read more about Colloq: Edward Glaeser & Nikhil Naik
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...

Read more about Colloq: Kristian Lum
2016 Nov 29

STAT 300: Natesh Pillai

12:00pm to 1:00pm

Location: 

Science Center Rm. 705
Accelerating MCMC algorithms for computer models
2017 Apr 17

Colloq: Ya Xu

4:15pm to 5:15pm

Location: 

Science Center Hall E
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