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.

Publication Details

Journal
Zurich Open Repository and Archive (University of Zurich)
Published
2024-09-30
DOI
https://doi.org/10.5167/uzh-267366
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
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article

Additive Bayesian Networks

Zurich Open Repository and Archive (University of Zurich)
Bayesian Modeling and Causal Inference
article

Additive Bayesian Networks

article en

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.

Zurich Open Repository and Archive (University of Zurich)
Openalex Percentile: Top 57%
Bayesian Modeling and Causal Inference
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Additive Bayesian Networks · Zurich Open Repository and Archive (University of Zurich) (2024) | TGRS Research Map | TGRS