37 citations · 141 across the 10 of their papers we have counts for
10 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…
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…
The process of macroprudential oversight in Europe
Peter Sarlin, Henrik J. Nyman
The 2007--2008 financial crisis has paved the way for the use of macroprudential policies in supervising the financial system as a whole. This paper views macroprudential oversight…
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…
Learning Chordal Markov Networks by Constraint Satisfaction
Jukka Corander, Tomi Janhunen, Jussi Rintanen +2
We investigate the problem of learning the structure of a Markov network from data. It is shown that the structure of such networks can be described in terms of constraints which e…
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…
From Bits to Atoms: 3D Printing in the Context of Supply Chain Strategies
Henrik J Nyman, Peter Sarlin
A lot of attention in supply chain management has been devoted to understanding customer requirements. What are customer priorities in terms of price and service level, and how can…