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04/20/2022
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Machine learning is increasingly used to discover diagnostic and prognos...
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03/29/2021
Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Markov networks are popular models for discrete multivariate systems whe...
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11/03/2020
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Learning vector autoregressive models from multivariate time series is c...
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09/30/2020
Towards Scalable Bayesian Learning of Causal DAGs
We give methods for Bayesian inference of directed acyclic graphs, DAGs,...
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10/30/2019
Learning pairwise Markov network structures using correlation neighborhoods
Markov networks are widely studied and used throughout multivariate stat...
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01/14/2019
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Learning the undirected graph structure of a Markov network from data is...
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02/25/2016
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood
We propose a Bayesian approximate inference method for learning the depe...
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01/31/2014
Marginal and simultaneous predictive classification using stratified graphical models
An inductive probabilistic classification rule must generally obey the p...
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01/20/2014
Marginal Pseudo-Likelihood Learning of Markov Network structures
Undirected graphical models known as Markov networks are popular for a w...
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10/04/2013
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models
We introduce a novel class of labeled directed acyclic graph (LDAG) mode...
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09/25/2013
