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Analyzing the Simonshaven Case Using Bayesian Networks.

Norman Fenton¹, Martin Neil¹, Barbaros Yet²

¹School of Electronic Engineering and Computer Science, Queen Mary University of London.

Topics in Cognitive Science

|March 13, 2019

Summary

This summary is machine-generated.

This study applies a Bayesian network (BN) model to the Simonshaven murder case, yielding a 74% posterior probability of guilt. Sensitivity analysis confirms the model

Keywords:

Bayesian networks Evidence Idioms Legal reasoning Probability Uncertainty

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Area of Science:

Computational Criminology
Legal Analytics
Bayesian Modeling

Background:

The Simonshaven murder case requires rigorous analysis.
Multiple modeling approaches are being explored for this case.
Bayesian networks (BNs) offer a structured method for evidence evaluation.

Purpose of the Study:

To construct a useful Bayesian network (BN) for the Simonshaven murder case.
To assess the feasibility of rapidly building a BN using an idioms-based approach.
To determine the posterior probability of guilt based on evidence.

Main Methods:

A Bayesian network (BN) was developed using an idioms-based approach.
Subjective judgments defined the graph structure and prior probabilities.
Sensitivity analysis evaluated the robustness of the model's conclusions.

Main Results:

The BN model yielded a 74% posterior probability of guilt.
The model demonstrated reasonable robustness across a range of subjective priors.
Conclusions generally remained below the 95% criminal law threshold.

Conclusions:

A BN can be rapidly constructed for legal cases.
The model provides a quantifiable assessment of guilt probability.
Further refinements can incorporate supplementary case information.