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 ...

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 ...

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 ...

On Friday the 24th of January 2020, M.Sc. Janne Leppä-aho will defend his doctoral thesis on Methods for Learning Directed and Undirected Graphical Models. The thesis is a part of research done in the ...