research
∙
06/20/2023
A Bayesian Take on Gaussian Process Networks
Gaussian Process Networks (GPNs) are a class of directed graphical model...
share
research
∙
05/05/2022
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions
Describing the causal relations governing a system is a fundamental task...
research
∙
12/16/2021
Marginalization in Bayesian Networks: Integrating Exact and Approximate Inference
Bayesian Networks are probabilistic graphical models that can compactly ...
research
∙
12/16/2021
The Dual PC Algorithm for Structure Learning
While learning the graphical structure of Bayesian networks from observa...
research
∙
07/08/2021
Benchpress: a scalable and platform-independent workflow for benchmarking structure learning algorithms for graphical models
Describing the relationship between the variables in a study domain and ...
research
∙
05/02/2021
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods f...
research
∙
10/29/2020
Learning Bayesian Networks from Ordinal Data
Bayesian networks are a powerful framework for studying the dependency s...
research
∙
04/20/2020
The Variance of Causal Effect Estimators for Binary V-structures
Adjusting for covariates is a well established method to estimate the to...
research
∙
03/21/2018
Efficient Structure Learning and Sampling of Bayesian Networks
Bayesian networks are probabilistic graphical models widely employed to ...
research
∙
04/20/2015
Partition MCMC for inference on acyclic digraphs
Acyclic digraphs are the underlying representation of Bayesian networks,...
research
∙
02/27/2014
Addendum on the scoring of Gaussian directed acyclic graphical models
We provide a correction to the expression for scoring Gaussian directed ...
research
∙
02/29/2012