37 citations · 188 across the 16 of their papers we have counts for
16 papers
The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference
Johan de Aguas, Sebastian Krumscheid, Johan Pensar +1
We define the Probability of Tiered Benefit in scenarios with a binary exposure and an outcome that is either categorical with ordered tiers or continuous partitioned by…
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation
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 tr…
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri +5
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental d…
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…
Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka, Antti Hyttinen, Johan Pensar +1
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov…
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Kimmo Suotsalo, Yingying Xu, Jukka Corander +1
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically ass…
Learning pairwise Markov network structures using correlation neighborhoods
Juri Kuronen, Jukka Corander, Johan Pensar
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from da…
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Johan Pensar, Yingying Xu, Santeri Puranen +3
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. As a result of the general appl…
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
Janne Leppä-aho, Johan Pensar, Teemu Roos +1
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression…
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…
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…