The likelihoo d ratio framework for evaluating evidence is becom ing more common in forensic practice. As a result \, the interest in Bayesian networks as a tool to analyse cases and performing computations has inc reased. However\, constructing a Bayesian network from scratch for every situation that one encoun ters is too costly. Therefore\, several researcher s have proposed Bayesian networks that correspond with frequent problems [1\,2]. These `building bl ocks'\; allow the user to only concentrate on the conditional probabilities that fit their part icular situation. This results in a more efficient workflow: the effort to construct the Bayesian n etwork is taken away. Furthermore\, it is no long er necessary that the user is experienced in const ructing Bayesian networks. However\, when the pro blem 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 clea r how one should model a certain problem\, regard less of the case specific details. For example\, a Bayesian network for a source level hypotheses p air where the evidence consists of a DNA profile has the same structure for any number of loci. Eac h locus can be added as a node together with it&# 39\;s corresponding drop-out/drop-in probabilitie s. For these type of problems\, one can take away the effort of constructing the network. This faci litates the practical application of Bayesian net works for forensic casework. We will show an examp le of `generating Bayesian networks'\; for a p roblem from crime linkage. In [3] a structure for modeling crime linkage with Bayesian networks is introduced. This structure is implemented in R wh ich allows the user to insert the parameters corre sponding to their situation (e.g. the number of c rimes/number of different types of evidence). Sub sequently\, this network can be used to obtain pos terior probabilities or likelihood ratios. We wil l show how this is useful in casework. LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR