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Generating Bayesian networks in Forensic Science An example from crime linkage
http://sms.cam.ac.uk/media/2333336
de Zoete, J
Wednesday 28th September 2016  13:30 to 14:15
40
Generating Bayesian networks in Forensic Science An example from crime linkage
http://sms.cam.ac.uk/media/2333336
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Generating Bayesian networks in Forensic Science An example from crime linkage
ucs_sms_2313931_2333336
http://sms.cam.ac.uk/media/2333336
Generating Bayesian networks in Forensic Science An example from crime linkage
de Zoete, J
Wednesday 28th September 2016  13:30 to 14:15
Wed, 05 Oct 2016 12:17:14 +0100
Isaac Newton Institute
de Zoete, J
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de Zoete, J
Wednesday 28th September 2016  13:30 to 14:15
de Zoete, J
Wednesday 28th September 2016  13:30 to 14:15
Cambridge University
2166
http://sms.cam.ac.uk/media/2333336
Generating Bayesian networks in Forensic Science An example from crime linkage
de Zoete, J
Wednesday 28th September 2016  13:30 to 14:15
Coauthor: Marjan Sjerps (Netherlands Forensic Institute)
The likelihood ratio framework for evaluating evidence is becoming more common in forensic practice. As a result, the interest in Bayesian networks as a tool to analyse cases and performing computations has increased. However, constructing a Bayesian network from scratch for every situation that one encounters is too costly. Therefore, several researchers have proposed Bayesian networks that correspond with frequent problems [1,2]. These `building blocks' allow the user to only concentrate on the conditional probabilities that fit their particular situation. This results in a more efficient workflow: the effort to construct the Bayesian network is taken away. Furthermore, it is no longer necessary that the user is experienced in constructing Bayesian networks. However, when the problem does not follow the `exact' assumptions of the building block, the Bayesian network can only serve as a starting point when constructing a model that does. In some situations, it is clear how one should model a certain problem, regardless of the case specific details. For example, a Bayesian network for a source level hypotheses pair where the evidence consists of a DNA profile has the same structure for any number of loci. Each locus can be added as a node together with it's corresponding dropout/dropin probabilities. For these type of problems, one can take away the effort of constructing the network. This facilitates the practical application of Bayesian networks for forensic casework. We will show an example of `generating Bayesian networks' for a problem from crime linkage. In [3] a structure for modeling crime linkage with Bayesian networks is introduced. This structure is implemented in R which allows the user to insert the parameters corresponding to their situation (e.g. the number of crimes/number of different types of evidence). Subsequently, this network can be used to obtain posterior probabilities or likelihood ratios. We will show how this is useful in casework.
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