Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment

Smid, Joost; Verloo, Didier; Barker, G. C.; Havelaar, Arie H.

doi:10.1016/j.ijfoodmicro.2009.12.015

Cited by 76 publications

(51 citation statements)

References 26 publications

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“…A more detailed analysis of the relative strengths and weaknesses of MC and BN approaches can be found in (Smid et al, 2010) (Nash & Hannah 2011). With the exception of the training of BNs during continued operation, we have demonstrated the value of these aspects of the model in this paper.…”

Section: Weaknessesmentioning

confidence: 69%

“…An MC model is also a probabilistic/stochastic model, and captures the process from input variables (farm data, in our case) to outputs (GHGe in our case). However, as discussed in (Smid et al, 2010) MC models have a number of limitations to the scope for modelling the often complex relationships between variables. In contrast, in a BN model, the whole set of variables (the joint probability distribution) is represented as a directed acyclic graph.…”

Section: Weaknessesmentioning

confidence: 99%

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Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

Pérez-Miñana

Krause

Thornton

2012

Environmental Modelling & Software

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Section: Weaknessesmentioning

confidence: 69%

Section: Weaknessesmentioning

confidence: 99%

Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

Pérez-Miñana

Krause

Thornton

2012

Environmental Modelling & Software

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“…The output distributions will reflect the combined effects of this input uncertainty over the specified ranges. The Monte Carlo simulation is not a new technique related to the risk assessment field, some authors have used this technique in order to address the uncertainty in this kind of studies [10,11,12].…”

Section: Uncertainty Characterization Through Monte Carlo Simulationmentioning

confidence: 99%

Introduction of the Human Factor Into the Risk Assessment Using Monte Carlo Simulation

Dan¹,

Guix²,

Marti³

et al. 2015

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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The frequency of occurrence of an accident scenario is one of the key aspects to take into consideration in the field of risk assessment. Aspects such as the human factor, which is a major cause of undesired events in process industries, are usually not considered explicitly in the frequency estimation, mainly due to the uncertainty generated due to the lack of knowledge and the complexity associated to it.In this work, failure frequencies are modified by including the uncertainty generated by the human factor through the Monte Carlo simulation. This technique is one of the most commonly approaches used for uncertainty assessment and it is based on probability distribution functions that represent all the variables included in the model.The Monte Carlo simulation technique has been proved be very useful in the risk assessment field. The model takes into account the uncertainty and variability generated by several variables of the human factor such as the Organizational factor (Contracting, Training, Communication and Reporting), the Job Characteristics Factor (Workload Management, Environmental Conditions, Safety Equipment) and the Personal Characteristics Factor (Skills and Knowledge, Personal Behavior).As a first attempt to test the model, it has been applied to a real case study of a chemical plant, obtaining new frequency values for a selected scenario. Since the uncertainty generated by the human factor has now been taken in to account through Monte Carlo simulation, these new values are more realistic and accurate. As a result, an improvement of the final risk assessment is achieved.

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“…environmental policy studies (Wolfson et al 1996). A BBN is a graphical model for probabilistic relationships among a set of variables (Pearl 1993, Heckerman 1999 and gives a compact representation of reasoning under uncertainty by making reference to Bayes' rule for computing probabilistic inference (Smid et al 2010). BBNs offer many advantages.…”

Section: Introductionmentioning

confidence: 99%

“…BBNs offer many advantages. They readily handle incomplete data sets (Heckerman 1999), they concisely represent probabilistic relationships (Cooper 1990, Pearl 1993, and their graphical user interface makes the approach simple to use for non-experts (Smid et al 2010). Their drawback is that they do not allow for inclusion of direct feedbacks in the analysis, which limits their use in vulnerability assessments.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Cognitive Mapping in Livelihood Vulnerability Analysis

Murungweni¹,

Wijk²,

Andersson³

et al. 2011

E&S

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ABSTRACT. Feedback mechanisms are important in the analysis of vulnerability and resilience of social-ecological systems, as well as in the analysis of livelihoods, but how to evaluate systems with direct feedbacks has been a great challenge. We applied fuzzy cognitive mapping, a tool that allows analysis of both direct and indirect feedbacks and can be used to explore the vulnerabilities of livelihoods to identified hazards. We studied characteristics and drivers of rural livelihoods in the Great Limpopo Transfrontier Conservation Area in southern Africa to assess the vulnerability of inhabitants to the different hazards they face. The process involved four steps: (1) surveys and interviews to identify the major livelihood types; (2) description of specific livelihood types in a system format using fuzzy cognitive maps (FCMs), a semi-quantitative tool that models systems based on people's knowledge; (3) linking variables and drivers in FCMs by attaching weights; and (4) defining and applying scenarios to visualize the effects of drought and changing park boundaries on cash and household food security. FCMs successfully gave information concerning the nature (increase or decrease) and magnitude by which a livelihood system changed under different scenarios. However, they did not explain the recovery path in relation to time and pattern (e.g., how long it takes for cattle to return to desired numbers after a drought). Using FCMs revealed that issues of policy, such as changing situations at borders, can strongly aggravate effects of climate change such as drought. FCMs revealed hidden knowledge and gave insights that improved the understanding of the complexity of livelihood systems in a way that is better appreciated by stakeholders.

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Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment

Cited by 76 publications

References 26 publications

Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

Introduction of the Human Factor Into the Risk Assessment Using Monte Carlo Simulation

Application of Fuzzy Cognitive Mapping in Livelihood Vulnerability Analysis

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