GeNIe Bayesian Risk Model Assistance
Budget: €30 – €250 EUR
I already have the skeleton of a 20-plus–variable Bayesian Network in GeNIe Modeler from a previous scaffold-collapse study, so you will not be starting from zero. What I need now is a polished, academically sound risk model and a companion report that will satisfy a university Technical Risk Management course.
Current status
• Network structure drafted in GeNIe, variables and parent-child links agreed.
• Partial datasets in CSV/Excel are available, yet I still face three kinds of data gaps: missing data points, uncertain or inconsistent records, and data-format / structure issues.
Scope of work
You will guide and execute the completion of the model by:
1. Finalising conditional probability tables (CPTs) for every node, applying appropriate imputation or expert-judgement techniques where data are incomplete or inconsistent.
2. Running comprehensive scenario analysis (baseline, best-case, worst-case, and any course-specified scenarios) and extracting the numeric and graphical outputs directly from GeNIe.
3. Conducting sensitivity analysis, including tornado diagrams and key-driver identification, to quantify variable influence on top-level risk.
4. Drafting a publish-ready academic report of at least 30 pages (Word, APA 7), covering methodology, data treatment, results, discussion, limitations, and recommendations. Graphics and tables generated in GeNIe should be seamlessly integrated.
Deliverables
• The completed GeNIe model file (.xdsl or .gnet) with fully populated CPTs and embedded documentation.
• All scenario and sensitivity exports in both PNG and CSV.
• A ≥30-page Word report with references, figures, and appendices.
Acceptance criteria
The network must execute without errors in GeNIe 3 .x, all results must be reproducible from the supplied data, and the report must meet length and citation requirements while demonstrating clear linkage between model construction, analysis, and conclusions.
Timeline
Two-week turnaround from award, with an interim milestone once CPTs are locked so I can validate assumptions before the analyses proceed. Interim feedback will be rapid to keep momentum.
If you are experienced with GeNIe, Bayesian Networks, and academic writing, this collaboration should be straightforward and rewarding.
Previous Supervisor Feedback to Be Addressed
The previous submission was not accepted because several course requirements were not sufficiently fulfilled. The revised model and report must explicitly correct the following points:
Basic Risk Structure and Mitigation Measures
The model must follow the basic structure of a technical risk as provided in the Moodle course materials. Mitigation measures must be clearly modeled and explained, not only mentioned generally.
Relationships Between Random Variables
The dependencies between the random variables must be revised according to the methodological guidance in Oepping & Hardy (2016), especially pp. 6–7. Particular attention must be given to the nodes “Structural Instability” and “Supervision Quality,” where the previous model included too many influencing factors. The revised network should reduce unnecessary complexity and justify every parent-child relationship.
Relevant States of Random Variables
The states of each random variable must be clearly identified and described according to Oepping & Hardy (2016), especially pp. 15–16. Each state must have a precise technical meaning, for example “low/high,” “stable/unstable,” or “adequate/inadequate,” with no vague or overlapping definitions.
Probability Estimation and CPT Consistency
The probability estimation method must be made more transparent and technically defensible. The revised report must explain the mechanisms of effect behind the conditional probabilities, ensure consistency across CPTs, and specifically review nodes such as “Supervision Quality” and “Structural Instability.”
Literature and Expert-Judgement Support
The report must avoid unsupported statements such as “probabilities were derived from literature-based assumptions and expert judgments” unless the exact literature sources and expert-judgement process are documented. All probability assumptions must be traceable to either data, cited literature, clearly explained expert judgement, or justified engineering reasoning.
Required Correction Objective
The final submission must show clearly how each of the above weaknesses has been corrected. A short “Response to Previous Feedback” section should be included in the report, mapping each supervisor comment to the specific improvement made in the revised Bayesian Network and documentation.
Current status
• Network structure drafted in GeNIe, variables and parent-child links agreed.
• Partial datasets in CSV/Excel are available, yet I still face three kinds of data gaps: missing data points, uncertain or inconsistent records, and data-format / structure issues.
Scope of work
You will guide and execute the completion of the model by:
1. Finalising conditional probability tables (CPTs) for every node, applying appropriate imputation or expert-judgement techniques where data are incomplete or inconsistent.
2. Running comprehensive scenario analysis (baseline, best-case, worst-case, and any course-specified scenarios) and extracting the numeric and graphical outputs directly from GeNIe.
3. Conducting sensitivity analysis, including tornado diagrams and key-driver identification, to quantify variable influence on top-level risk.
4. Drafting a publish-ready academic report of at least 30 pages (Word, APA 7), covering methodology, data treatment, results, discussion, limitations, and recommendations. Graphics and tables generated in GeNIe should be seamlessly integrated.
Deliverables
• The completed GeNIe model file (.xdsl or .gnet) with fully populated CPTs and embedded documentation.
• All scenario and sensitivity exports in both PNG and CSV.
• A ≥30-page Word report with references, figures, and appendices.
Acceptance criteria
The network must execute without errors in GeNIe 3 .x, all results must be reproducible from the supplied data, and the report must meet length and citation requirements while demonstrating clear linkage between model construction, analysis, and conclusions.
Timeline
Two-week turnaround from award, with an interim milestone once CPTs are locked so I can validate assumptions before the analyses proceed. Interim feedback will be rapid to keep momentum.
If you are experienced with GeNIe, Bayesian Networks, and academic writing, this collaboration should be straightforward and rewarding.
Previous Supervisor Feedback to Be Addressed
The previous submission was not accepted because several course requirements were not sufficiently fulfilled. The revised model and report must explicitly correct the following points:
Basic Risk Structure and Mitigation Measures
The model must follow the basic structure of a technical risk as provided in the Moodle course materials. Mitigation measures must be clearly modeled and explained, not only mentioned generally.
Relationships Between Random Variables
The dependencies between the random variables must be revised according to the methodological guidance in Oepping & Hardy (2016), especially pp. 6–7. Particular attention must be given to the nodes “Structural Instability” and “Supervision Quality,” where the previous model included too many influencing factors. The revised network should reduce unnecessary complexity and justify every parent-child relationship.
Relevant States of Random Variables
The states of each random variable must be clearly identified and described according to Oepping & Hardy (2016), especially pp. 15–16. Each state must have a precise technical meaning, for example “low/high,” “stable/unstable,” or “adequate/inadequate,” with no vague or overlapping definitions.
Probability Estimation and CPT Consistency
The probability estimation method must be made more transparent and technically defensible. The revised report must explain the mechanisms of effect behind the conditional probabilities, ensure consistency across CPTs, and specifically review nodes such as “Supervision Quality” and “Structural Instability.”
Literature and Expert-Judgement Support
The report must avoid unsupported statements such as “probabilities were derived from literature-based assumptions and expert judgments” unless the exact literature sources and expert-judgement process are documented. All probability assumptions must be traceable to either data, cited literature, clearly explained expert judgement, or justified engineering reasoning.
Required Correction Objective
The final submission must show clearly how each of the above weaknesses has been corrected. A short “Response to Previous Feedback” section should be included in the report, mapping each supervisor comment to the specific improvement made in the revised Bayesian Network and documentation.