9 citations · 21 across the 3 of their papers we have counts for
4 papers
Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Johan Pensar, Henrik Nyman, Jukka Corander
Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressi…
Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka, Antti Hyttinen, Johan Pensar +1
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov…
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Kimmo Suotsalo, Yingying Xu, Jukka Corander +1
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically ass…
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Johan Pensar, Yingying Xu, Santeri Puranen +3
Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general appl…