37 citations · 141 across the 10 of their papers we have counts for
5 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…
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…