Search Results for author: Johan Pensar

Found 14 papers, 3 papers with code

Uncertainty quantification in automated valuation models with locally weighted conformal prediction

no code implementations11 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.

Conformal Prediction Uncertainty Quantification

DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

1 code implementation6 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.

BIG-bench Machine Learning Causal Inference

Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood

no code implementations29 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.

Towards Scalable Bayesian Learning of Causal DAGs

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.

Bayesian Inference

Learning pairwise Markov network structures using correlation neighborhoods

1 code implementation30 Oct 2019 Juri Kuronen, Jukka Corander, Johan Pensar

Markov networks are widely studied and used throughout multivariate statistics and computer science.

High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study

no code implementations14 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.

Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood

no code implementations25 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.

Context-specific independence in graphical log-linear models

no code implementations9 Sep 2014 Henrik Nyman, Johan Pensar, Timo Koski, Jukka Corander

Log-linear models are the popular workhorses of analyzing contingency tables.

Marginal and simultaneous predictive classification using stratified graphical models

no code implementations31 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.

Classification General Classification

Marginal Pseudo-Likelihood Learning of Markov Network structures

no code implementations20 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.

Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models

no code implementations4 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.

Stratified Graphical Models - Context-Specific Independence in Graphical Models

no code implementations25 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.

Computer Security

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