no code implementations • 11 Dec 2023 • Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams
Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncertainty.
1 code implementation • 6 May 2022 • Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth.
1 code implementation • 20 Apr 2022 • Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig M. Sollid, Victor Greiff, Geir Kjetil Sandve
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data.
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 • NeurIPS 2020 • Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data.
1 code implementation • 30 Oct 2019 • Juri Kuronen, Jukka Corander, Johan Pensar
Markov networks are widely studied and used throughout multivariate statistics and computer science.
no code implementations • 14 Jan 2019 • Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander
Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades.
no code implementations • 25 Feb 2016 • Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.
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.