Description 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 ...

Credal networks: Graphical models that extend Bayesian networks by associating each node with a set of probability distributions, thereby capturing uncertainty more robustly.

We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general ...

In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, ...

COMP_SCI 474: Probabilistic Graphical Models This course is not currently offered. Prerequisites COMP_SCI 349 or permission of the instructor Description Probabilistic graphical models are a powerful ...