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.
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...
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...
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...
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...
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...
