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 ...

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 ...

A recent announcement appearing in MIT News, “Machine learning branches out,” highlights new research in probabilistic graphical models. In a paper being presented in December at the annual conference ...

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 ...

What Is A Probabilistic Model? A probabilistic model is a statistical tool that accounts for randomness or uncertainty when predicting future events. Instead of giving a definitive answer, it ...

In systems that exhibit artificial intelligence (AI), an agent at its centre has to learn and represent a model of its environment, reason about it, and decide on its actions. A possible approach to ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...