no code implementations • 29 Mar 2021 • 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.
no code implementations • 9 Sep 2014 • Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander
Log-linear models are the popular workhorses of analyzing contingency tables.
no code implementations • 31 Jan 2014 • Henrik Nyman, Jie Xiong, Johan Pensar, Jukka Corander
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution.
no code implementations • 20 Jan 2014 • Johan Pensar, Henrik Nyman, Juha Niiranen, Jukka Corander
Traditionally, learning of the network structure has been done under the assumption of chordality which ensures that efficient scoring methods can be used.
no code implementations • 4 Oct 2013 • Johan Pensar, Henrik Nyman, Timo Koski, Jukka Corander
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables.
no code implementations • NeurIPS 2013 • Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
We investigate the problem of learning the structure of a Markov network from data.
no code implementations • 25 Sep 2013 • Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander
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 mapping of diseases, credit risk evaluation, reliability and computer security, etc.