Large engineering project risk management using a Bayesian belief network

Lee, Eunchang; Park, Yongtae; Shin, Jong Gye

doi:10.1016/j.eswa.2008.07.057

Cited by 236 publications

(138 citation statements)

References 37 publications

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“…(2) BBN may be developed using expert opinion instead of requiring historical data. (3) BBN gains insight into relationships among variables of the process due to its graphical display (Lee et al 2008). (4) Properly designed BBN can provide a valid output when any subset of the modeled variables is present.…”

Section: Discussionmentioning

confidence: 99%

“…A Bayesian approach finds the optimal model structure from data after a BBN is constructed by the user's priori knowledge, and a constraint-based approach finds the optimal model structure from conditional dependences in each pair of variables. However, a constraint-based approach is commonly used due to its computational simplicity compared to the Bayesian approach (Lee et al 2008). So in the study we applied constraint-based approach to construct the network.…”

Section: Structural Learningmentioning

confidence: 99%

“…The structural learning of BBN, so-called qualitative analysis, is the graphical representation of independence holding among variables and has the form of an acyclic directed graph (Lee et al 2008). The purpose of this phase is to identify significant environmental factors being applicable to assess the NTDs risk.…”

Section: Structural Learningmentioning

confidence: 99%

“…The parameter learning part of BBN, the so-called quantitative analysis, finds dependence relations as joint conditional probability distributions among variables using cause and consequence relationships from the qualitative part and data of variables (Lee et al 2008). The dependence relations can be assigned by expert knowledge.…”

Section: Quantitative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Risk assessment of human neural tube defects using a Bayesian belief network

Liao

Wang

Guo

et al. 2009

Stoch Environ Res Risk Assess

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Neural tube defects (NTDs) constitute the most common type of birth defects. How much risk of NTDs could an area take? The answer to this question will help people understand the geographical distribution of NTDs and explore its environmental causes. Most existing methods usually take the spatial correlation of cases into account and rarely consider the effect of environmental factors. However, especially in rural areas, the NTDs cases have a little effect on each other across space, whereas the role of environmental factors is significant. To demonstrate these points, Heshun, a county with the highest rate of NTDs in China, was selected as the region of interest in the study. Bayesian belief network was used to quantify the probability of NTDs occurred at villages with no births. The study indicated that the proposed method was easy to apply and high accuracy was achieved at a 95% confidence level.

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Section: Discussionmentioning

confidence: 99%

Section: Structural Learningmentioning

confidence: 99%

Section: Structural Learningmentioning

confidence: 99%

Section: Quantitative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Risk assessment of human neural tube defects using a Bayesian belief network

Liao

Wang

Guo

et al. 2009

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Few specific methods are able to model risk correlations with a network structure. Several papers on the application of the Bayesian belief network (BBN) have appeared in recent years in the field of project risk management (Fan and Yu 2004, Lee et al 2008, Trucco et al 2008), which could model risk interrelations, from multiple inputs to multiple outputs. Nevertheless, BBN demands oriented links, is inherently acyclic, and hence does not easily model the loop phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Fang

Marle

2013

Journal of Engineering Design

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Bayesian Networks in Project Management

Yet¹

2017

Wiley StatsRef: Statistics Reference Online

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Bayesian networks (BNs) offer unique benefits for combining data and expert knowledge to model complex joint probability distributions. Recent advances in inference algorithms enabled efficient computation of BNs with both discrete and continuous variables that are also called hybrid BNs . Consequently, BNs have been widely used as risk assessment and decision support tools in various domains including project management. This article illustrates the use of BNs in different aspects of project management and gives an overview of the relevant studies in this domain.

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Large engineering project risk management using a Bayesian belief network

Cited by 236 publications

References 37 publications

Risk assessment of human neural tube defects using a Bayesian belief network

Risk assessment of human neural tube defects using a Bayesian belief network

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Bayesian Networks in Project Management

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