<b>BayesNetBP</b>: An <i>R</i> Package for Probabilistic Reasoning in Bayesian Networks

Yu, Han; Moharil, Janhavi; Blair, Rachael Hageman

doi:10.18637/jss.v094.i03

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…By utilizing cyjShiny developers can both develop methodologies and web applications utilizing only R code thereby lowering the barrier to creating such web-based interactive tools. This strategy has been employed by the developers of BayesNetBP that utilize R Shiny for the exploration of results to their method analyzing Bayesian networks [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis

et al. 2023

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Section: Methodsmentioning

confidence: 99%

cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis

et al. 2023

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“…As suggested by Madsen [12], we applied exchange operations to eliminate both continuous and discrete variables during message passing. We conducted multiple simulation experiments, comparing our method with two existing methods: CGBayesNets [15] and BayesNetBP [16]. The results indicate that our method outperforms the existing methods in computational speed.…”

Section: Introductionmentioning

confidence: 97%

Propagation Computation for Mixed Bayesian Networks Using Minimal Strong Triangulation

Liu,

Wang,

Zhou

et al. 2024

Mathematics

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In recent years, mixed Bayesian networks have received increasing attention across various fields for probabilistic reasoning. Though many studies have been devoted to propagation computation on strong junction trees for mixed Bayesian networks, few have addressed the construction of appropriate strong junction trees. In this work, we establish a connection between the minimal strong triangulation for marked graphs and the minimal triangulation for star graphs. We further propose a minimal strong triangulation method for the moral graph of mixed Bayesian networks and develop a polynomial-time algorithm to derive a strong junction tree from this minimal strong triangulation. Moreover, we also focus on the propagation computation of all posteriors on this derived strong junction tree. We conducted multiple numerical experiments to evaluate the performance of our proposed method, demonstrating significant improvements in computational efficiency compared to existing approaches. Experimental results indicate that our minimal strong triangulation approach provides a robust framework for efficient probabilistic inference in mixed Bayesian networks.

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“…To our knowledge, in addition to jti, there are three other packages for belief propagation in R; gRain (Højsgaard 2012), BayesNetBP (Yu, Moharil, and Blair 2020) and RHugin (Konis 2014) where the latter is not on the Comprehensive R Archive Network (CRAN). The only R package on CRAN that has an API for (dense) table operations is gRbase, which gRain depends upon.…”

Section: Introductionmentioning

confidence: 99%

sparta: Sparse Tables and their Algebra with a View Towards High Dimensional Graphical Models

Lindskou,

Højsgaard,

Eriksen

et al. 2021

Preprint

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A Bayesian network is a multivariate (potentially high dimensional) probabilistic model, which is often formed by combining lower dimensional components. Inference (computation of conditional probabilities) is based on message passing algorithms that utilize conditional independence structures. In Bayesian networks for discrete variables with finite state spaces, there is a fundamental problem in high dimensions: A discrete distribution is represented by a table of values, and in high dimensions such tables can become prohibitively large. In inference, such tables must be multiplied which can lead to even larger tables. The sparta package tries to meet this challenge by introducing new methods that efficiently handles multiplication and marginalization of sparse tables. The package was written in the R programming language and is freely available from the Comprehensive R Archive Network (CRAN). The companion package jti, also on CRAN, was developed to showcase the potential of sparta in connection to the Junction Tree Algorithm. We show that sparta outperforms existing methods for high-dimensional sparse probability tables. Furthermore, we demonstrate, that sparta can handle problems which are otherwise infeasible due to lack of computer memory.

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BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks

Cited by 9 publications

References 45 publications

cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis

cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis

Propagation Computation for Mixed Bayesian Networks Using Minimal Strong Triangulation

sparta: Sparse Tables and their Algebra with a View Towards High Dimensional Graphical Models

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