130 citations · 671 across the 42 of their papers we have counts for
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stat.ML2014
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
Tom Claassen, Joris M. Mooij, Tom Heskes
This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a…
q-bio.NC2014★ 1 cited
Efficient sampling of Gaussian graphical models using conditional Bayes factors
Max Hinne, Alex Lenkoski, Tom Heskes +1
Bayesian estimation of Gaussian graphical models has proven to be challenging because the conjugate prior distribution on the Gaussian precision matrix, the G-Wishart distribution,…
cs.LG2014★ 8 cited
Properties of Bethe Free Energies and Message Passing in Gaussian Models
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractiona…