As we devise more complicated prior distributions, will inference algorithms keep up? We highlight a negative result in computable probability theory by Ackerman, Freer, and Roy (...
— This work deals with a group of mobile sensors sampling a spatiotemporal random field whose mean is unknown and covariance is known up to a scaling parameter. The Bayesian pos...
In this paper we obtain convergence bounds for the concentration of Bayesian posterior distributions (around the true distribution) using a novel method that simplifies and enhan...
We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants from their head pose and contextual cues. The main contribution of the paper is ...
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...