1 code implementation • 8 Aug 2017 • Janne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables.
no code implementations • 8 Mar 2016 • Otte Heinävaara, Janne Leppä-aho, Jukka Corander, Antti Honkela
Various $\ell_1$-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation.
no code implementations • 25 Feb 2016 • Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.
