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 ...

Demetris Lamnisos, Jim E. Griffin, Mark F. J. Steel, Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models, Journal of Computational and Graphical Statistics, Vol. 22, No. 3, ...

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, ...

Chordal Markov networks are a central class of undirected graphical models. Being equivalent to so-called decomposable models, they are essentially a special case of Bayesian networks. This thesis ...

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 ...