20 citations · 39 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 19 cited
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
Tomi Silander, Janne Leppä-aho, Elias Jääsaari +1
We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely rela…
stat.ML2016★ 20 cited
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
Janne Leppä-aho, Johan Pensar, Teemu Roos +1
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression…