Graphical models in RL (Reinforcement Learning) fuse domain-specific information and data, boosting performance. RL spans robotics, NLP, computer vision, and recommendation systems.

We study conditions that allow accurate graphical model selection from non-stationary data. The observed data is modelled as a vector-valued zero-mean Gaussian random process whose samples are ...

Directed graphical models, also known as Bayesian networks or belief networks, use directed acyclic graphs (DAGs) to encode conditional dependencies among random variables. A DAG is a graph where ...

In our paper, “Neural Graphical Models (opens in new tab),” presented at ECSQARU 2023 (opens in new tab), we propose Neural Graphical Models (NGMs), a new type of PGM that learns to represent the ...

Traditional sample covariance matrices may be ill-conditioned or non-invertible, necessitating modern methods that incorporate regularisation and sparsity. ... Graphical Model: ...

sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression. Science China Press. Journal National Science Review DOI ...

A five-minute formula from Alexander Denev that takes you through a simple probabilistic graphical model and explains how and why these are used. Find out more about the ground-breaking book, ...

Models identified and fitted using the variance-correlation decomposition of a covariance matrix are not guaranteed to be positive definite, but those using the modified Cholesky decomposition are.