Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular, ...

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, ...

Gaussian graphical models have received considerable attention during the past four decades from the statistical and machine learning communities. In Bayesian treatments of this model, the G-Wishart ...

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) Disease progression can be classified into three states: the normal stage, pre-disease stage and disease stage, with the pre-disease stage representing a critical threshold just before the onset ...