37 citations · 188 across the 16 of their papers we have counts for
7 papers · 1 filter
Learning pairwise Markov network structures using correlation neighborhoods
Juri Kuronen, Jukka Corander, Johan Pensar
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from da…
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…
Context-specific independence in graphical log-linear models
Henrik Nyman, Johan Pensar, Timo Koski +1
Log-linear models are the popular workhorses of analyzing contingency tables. A log-linear parameterization of an interaction model can be more expressive than a direct parameteriz…
Marginal and simultaneous predictive classification using stratified graphical models
Henrik Nyman, Jie Xiong, Johan Pensar +1
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are…
Marginal Pseudo-Likelihood Learning of Markov Network structures
Johan Pensar, Henrik Nyman, Juha Niiranen +1
Undirected graphical models known as Markov networks are popular for a wide variety of applications ranging from statistical physics to computational biology. Traditionally, learni…
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models
Johan Pensar, Henrik Nyman, Timo Koski +1
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables. LDAGs generalize earlier proposals for allowing local structures i…
Stratified Graphical Models - Context-Specific Independence in Graphical Models
Henrik Nyman, Johan Pensar, Timo Koski +1
Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic…