Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that ...

Increasingly complex mobile applications require better and more accurate methods of automatedvGUI testing. Traditional testing frameworks relying on fixed delays or pixel-based image ...

Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a ...

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

Probabilistic inference depends exponentially on the so called tree width, which is a measure of the worst-case intermediate result during inference that is bounded from below by the maximum number of ...