Additive Bayesian Networks

The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model.BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.They can derive a directed acyclic graph from empirical data to describe the dependency structure between random variables.Additive Bayesian Network (ABN) models extend the concept of generalized linear models, typically used for predicting a single outcome, to scenarios with multiple dependent variables (e.g., Kratzer et al. (2023)).This makes them a powerful tool for understanding complex, multivariate datasets.This package provides routines for structure learning and parameter estimation of ABN models.

Authors

Publication Details

Journal
The Journal of Open Source Software
Published
2024-09-30
DOI
https://doi.org/10.21105/joss.06822
Citations
3
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

Additive Bayesian Networks

Reinhard Furrer, Georg Spinner, Matteo Delucchi, Jonas I. Liechti
3 citations
The Journal of Open Source Software
Bayesian Modeling and Causal Inference
1.04
article

Additive Bayesian Networks

Reinhard Furrer, Georg Spinner, Matteo Delucchi, Jonas I. Liechti
article en
3 citations

Abstract

The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model.BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.They can derive a directed acyclic graph from empirical data to describe the dependency structure between random variables.Additive Bayesian Network (ABN) models extend the concept of generalized linear models, typically used for predicting a single outcome, to scenarios with multiple dependent variables (e.g., Kratzer et al. (2023)).This makes them a powerful tool for understanding complex, multivariate datasets.This package provides routines for structure learning and parameter estimation of ABN models.

The Journal of Open Source SoftwareVol. 9(101)
Zürcher Hochschule für Angewandte Wissenschaften
Openalex Percentile: Top 19%
Bayesian Modeling and Causal Inference
1.04
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