130 citations · 671 across the 42 of their papers we have counts for
6 papers · 1 filter
IPF for Discrete Chain Factor Graphs
Wim Wiegerinck, Tom Heskes
Iterative Proportional Fitting (IPF), combined with EM, is commonly used as an algorithm for likelihood maximization in undirected graphical models. In this paper, we present two i…
Expectation Propogation for approximate inference in dynamic Bayesian networks
Tom Heskes, Onno Zoeter
We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl s exact belief propagation.Expectation propagation IS a g…
Approximate Inference and Constrained Optimization
Tom Heskes, Kees Albers, Hilbert Kappen
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspond…
A Bayesian Approach to Constraint Based Causal Inference
Tom Claassen, Tom Heskes
We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical gu…
Bounds on the Bethe Free Energy for Gaussian Networks
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and T…
A Logical Characterization of Constraint-Based Causal Discovery
Tom Claassen, Tom Heskes
We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca…