Graphical Structure of Bayesian Networks by Eliciting Mental Models of Experts

2017 ◽  
pp. 333-341
Author(s):  
Udai Kumar Kudikyala ◽  
Mounika Bugudapu ◽  
Manasa Jakkula
Keyword(s):  
Bayesian Networks ◽  
Mental Models ◽  
Graphical Structure

scholarly journals Package ‘bayesvl’: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’

2019 ◽  
Author(s):  
Viet-Phuong La ◽  
Quan-Hoang Vuong
Keyword(s):  
Bayesian Networks ◽  
Graphical Structure

La VP, Vuong QH. (2019). Package 'bayesvl': Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'. The Comprehensive R Archive Network (CRAN); v.0.8.5 officially published on May 24, 2019.

A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks

2019 ◽  
Vol 234 (2) ◽  
pp. 422-436
Author(s):  
Bing Wu ◽  
Huibin Tian ◽  
Xinping Yan ◽  
C. Guedes Soares
Keyword(s):  
Bayesian Networks ◽  
Emergency Management ◽  
Yangtze River ◽  
Intensity Variation ◽  
Maritime Transportation ◽  
Major Type ◽  
Estimation Model ◽  
Domain Experts ◽  
Novel Approach ◽  
Graphical Structure

Collision is a major type of accident in maritime transportation, which in the downstream of Yangtze River is even more pronounced due to specific features that have significant impact on the collision consequence, including a special lane for small-sized ships, traffic intensity variation with the tide period, many restricted areas, and emergency resources spread along the river. This article models the collision consequences in the downstream of Yangtze River using Bayesian Networks, considering the causation factors and including a novel approach for the emergency management of maritime accidents. The graphical structure lies on domain experts and on previous works, while the conditional probability tables are developed from historical data. Both the graphical structure and parameters are validated using the well-known methods, which reflects that the developed model is reasonable. The merits of the proposed consequence estimation model that considers emergency management includes (1) a detailed description of the collision accident development and (2) consequence estimation result with good accuracy.

scholarly journals CRAN package bayesvl on CSIRO

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Bayesian Networks ◽  
Graphical Structure

bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

scholarly journals bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Networks ◽  
R Package ◽  
Open Science ◽  
Associated Functions ◽  
Science Framework ◽  
Graphical Structure

Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).

Methodology for Risk Assessment and Costs Associated with Risk Occurrence in E-Government Projects

2015 ◽  
pp. 349-369 ◽  
Author(s):  
Neven Vrček ◽  
Petra Peharda ◽  
Dušan Munđar
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Project Managers ◽  
New Approach ◽  
Management Quality ◽  
Government Project ◽  
Project Risks ◽  
Graphical Structure

The main purpose of this chapter is to emphasize the problem of e-government project risks and to introduce a methodology for risk assessment and calculation of costs associated with risk occurrence in e-government projects based on Bayesian networks. The proposed methodology presents a new approach to the assessment of risks and costs related to e-government project risks. As such, it facilitates the holistic decision making procedure for project managers. The application of Bayesian networks in the context of risks and risk related costs reduces the level of uncertainty in e-government projects and provides a graphical structure of risks and corresponding costs. Finally, the sensitivity analysis has also been integrated into the methodology and its results can have a significant impact on the overall project management quality.

Methodology for Risk Assessment and Costs Associated with Risk Occurrence in E-Government Projects

2015 ◽  
pp. 449-469
Author(s):  
Neven Vrček ◽  
Petra Peharda ◽  
Dušan Munđar
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Project Managers ◽  
New Approach ◽  
Management Quality ◽  
Government Project ◽  
Project Risks ◽  
Graphical Structure

The main purpose of this chapter is to emphasize the problem of e-government project risks and to introduce a methodology for risk assessment and calculation of costs associated with risk occurrence in e-government projects based on Bayesian networks. The proposed methodology presents a new approach to the assessment of risks and costs related to e-government project risks. As such, it facilitates the holistic decision making procedure for project managers. The application of Bayesian networks in the context of risks and risk related costs reduces the level of uncertainty in e-government projects and provides a graphical structure of risks and corresponding costs. Finally, the sensitivity analysis has also been integrated into the methodology and its results can have a significant impact on the overall project management quality.

scholarly journals CRAN package bayesvl on Microsoft

2021 ◽  
Author(s):  
AISDL
Keyword(s):  
Bayesian Networks ◽  
Graphical Structure

bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

scholarly journals bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

2020 ◽  
Author(s):  
AISDL
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Networks ◽  
R Package ◽  
Open Science ◽  
Associated Functions ◽  
Science Framework ◽  
Graphical Structure

Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate 'Stan' code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) The 'bayesvl' R package. Open Science Framework (May 18).Version: 0.8.5Depends: R (≥ 3.4.0), rstan (≥ 2.10.0), StanHeaders (≥ 2.18.0), stats, graphics, methodsImports: coda, bnlearn, ggplot2, bayesplot, viridis, reshape2, dplyrSuggests: loo (≥ 2.0.0)Published: 2019-05-24Author: Viet-Phuong La [aut, cre], Quan-Hoang Vuong [aut]Maintainer: Viet-Phuong La BugReports: https://github.com/sshpa/bayesvl/issuesLicense: GPL (≥ 3)URL: https://github.com/sshpa/bayesvl

scholarly journals CRAN R Package ‘bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan''

2019 ◽  
Author(s):  
Quan-Hoang Vuong ◽  
Viet-Phuong La
Keyword(s):  
Bayesian Networks ◽  
R Package ◽  
Reference Manual ◽  
Graphical Structure

Reference manual for R package "bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'" developed by Vuong Quan Hoang and Viet Phuong La. The package is published in The Comprehensive R Archive Network (CRAN). For more information: https://cran.r-project.org/web/packages/bayesvl/index.html

Methodology for Risk Assessment and Costs Associated with Risk Occurrence in E-Government Projects

2013 ◽  
pp. 252-272 ◽  
Author(s):  
Neven Vrcek ◽  
Petra Peharda ◽  
Dušan Mundar
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Sensitivity Analysis ◽  
Bayesian Networks ◽  
Project Managers ◽  
New Approach ◽  
Management Quality ◽  
Government Project ◽  
Project Risks ◽  
Graphical Structure

The main purpose of this chapter is to emphasize the problem of e-government project risks and to introduce a methodology for risk assessment and calculation of costs associated with risk occurrence in e-government projects based on Bayesian networks. The proposed methodology presents a new approach to the assessment of risks and costs related to e-government project risks. As such, it facilitates the holistic decision making procedure for project managers. The application of Bayesian networks in the context of risks and risk related costs reduces the level of uncertainty in e-government projects and provides a graphical structure of risks and corresponding costs. Finally, the sensitivity analysis has also been integrated into the methodology and its results can have a significant impact on the overall project management quality.

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