We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximumlikelihood) soluti...
Andrea Vedaldi, Hailin Jin, Paolo Favaro, Stefano ...
Two approaches to logic programming with probabilities emerged over time: bayesian reasoning and probabilistic satisfiability (PSAT). The attractiveness of the former is in tying ...
In order to succeed, agents playing games must reason about the mechanics of the game, the strategies of other agents, other agents’ reasoning about their strategies, and the ra...
Abstract. We present a Bayesian approach for simultaneously estimating the number of people in a crowd and their spatial locations by sampling from a posterior distribution over cr...
Incomplete decision algorithms can often solve larger problem instances than complete ones. The drawback is that one does not know whether the algorithm will finish soon, later, ...