37 citations · 188 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
Stratified Gaussian Graphical Models
Henrik Nyman, Johan Pensar, Jukka Corander
Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivari…
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…