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SUMMARY:Generating Bayesian networks in Forensic Science An example from c
 rime linkage - Jacob de Zoete (Universiteit van Amsterdam)
DTSTART:20160928T123000Z
DTEND:20160928T131500Z
UID:TALK67667@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Co-author: Marjan Sjerps (Netherlands Forensic  Institut
 e) <br></span> <span><br>The likelihood ratio framework for evaluating evi
 dence is becoming more  common in forensic practice. As a result\, the int
 erest in Bayesian networks as a  tool to analyse cases and performing comp
 utations has increased. However\,  constructing a Bayesian network from sc
 ratch 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&#39\;  allow the user to 
 only concentrate on the conditional probabilities that fit  their particul
 ar situation. This results in a more efficient workflow: the  effort to co
 nstruct 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&#39\; 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 cl
 ear how one should  model a certain problem\, regardless of the case speci
 fic details. For example\, a  Bayesian network for a source level hypothes
 es pair where the evidence consists  of a DNA profile has the same structu
 re for any number of loci. Each locus can  be added as a node together wit
 h it&#39\;s corresponding drop-out/drop-in  probabilities. For these type 
 of problems\, one can take away the effort of  constructing the network. T
 his facilitates the practical application of Bayesian  networks for forens
 ic casework. We will show an example of `generating Bayesian  networks&#39
 \; for a problem from crime linkage. In [3] a structure for modeling  crim
 e linkage with Bayesian networks is introduced. This structure is  impleme
 nted 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  pr
 obabilities or likelihood ratios. We will show how this is useful in  case
 work.</span>
LOCATION:Seminar Room 1\, Newton Institute
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