Johan Pensar
Johan Pensar
Associate Professor, Statistics and Data Science, University of Oslo
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The genetics of sexuality and aggression (GSA) twin samples in Finland
A Johansson, P Jern, P Santtila, B von der Pahlen, E Eriksson, ...
Twin Research and Human Genetics 16 (1), 150-156, 2013
Learning chordal Markov networks by constraint satisfaction
J Corander, T Janhunen, J Rintanen, H Nyman, J Pensar
Advances in Neural Information Processing Systems, 1349-1357, 2013
Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models
J Pensar, H Nyman, T Koski, J Corander
Data mining and knowledge discovery 29 (2), 503-533, 2015
Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis
G Méric, L Mageiros, J Pensar, M Laabei, K Yahara, B Pascoe, N Kittiwan, ...
Nature communications 9 (1), 1-11, 2018
Integrated analysis of population genomics, transcriptomics and virulence provides novel insights into Streptococcus pyogenes pathogenesis
P Kachroo, JM Eraso, SB Beres, RJ Olsen, L Zhu, W Nasser, PE Bernard, ...
Nature genetics 51 (3), 548-559, 2019
Stratified graphical models-context-specific independence in graphical models
H Nyman, J Pensar, T Koski, J Corander
Bayesian Analysis 9 (4), 883-908, 2014
Context-specific independence in graphical log-linear models
H Nyman, J Pensar, T Koski, J Corander
Computational Statistics 31 (4), 1493-1512, 2016
SuperDCA for genome-wide epistasis analysis
S Puranen, M Pesonen, J Pensar, YY Xu, JA Lees, SD Bentley, ...
Microbial genomics 4 (6), 2018
The role of local partial independence in learning of Bayesian networks
J Pensar, H Nyman, J Lintusaari, J Corander
International Journal of Approximate Reasoning 69, 91-105, 2016
Genome-wide epistasis and co-selection study using mutual information
J Pensar, S Puranen, B Arnold, N MacAlasdair, J Kuronen, G Tonkin-Hill, ...
Nucleic acids research 47 (18), e112-e112, 2019
A logical approach to context-specific independence
J Corander, A Hyttinen, J Kontinen, J Pensar, J Väänänen
Annals of Pure and Applied Logic, 2019
Learning Gaussian graphical models with fractional marginal pseudo-likelihood
J Leppä-aho, J Pensar, T Roos, J Corander
International Journal of Approximate Reasoning 83, 21-42, 2017
Learning discrete decomposable graphical models via constraint optimization
T Janhunen, M Gebser, J Rintanen, H Nyman, J Pensar, J Corander
Statistics and Computing 27 (1), 115-130, 2017
Marginal pseudo-likelihood learning of discrete Markov network structures
J Pensar, H Nyman, J Niiranen, J Corander
Bayesian analysis 12 (4), 1195-1215, 2017
Marginal and simultaneous predictive classification using stratified graphical models
H Nyman, J Xiong, J Pensar, J Corander
Advances in Data Analysis and Classification 10 (3), 305-326, 2016
A Bayesian decision-support tool for child sexual abuse assessment and investigation
A Tadei, J Pensar, J Corander, K Finnilä, P Santtila, J Antfolk
Sexual Abuse 31 (4), 374-396, 2019
Structure Learning for Bayesian Networks over Labeled DAGs
A Hyttinen, J Pensar, J Kontinen, J Corander
International Conference on Probabilistic Graphical Models, 133-144, 2018
Plasmids Shaped the Recent Emergence of the Major Nosocomial Pathogen Enterococcus faecium
S Arredondo-Alonso, J Top, A McNally, S Puranen, M Pesonen, J Pensar, ...
mBio 11 (1), 2020
Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome
S Arredondo-Alonso, J Top, AC Schürch, A McNally, S Puranen, ...
bioRxiv, 530725, 2019
Representing local structure in Bayesian networks by Boolean functions
Y Zou, J Pensar, T Roos
Pattern Recognition Letters 95, 73-77, 2017
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Artikelen 1–20