**Summary:**

**This article discusses the importance of crime linking, which is basically drawing the conclusion that the same offender is responsible for more than one crime, to law enforcement and describes a study in which Bayes' theorem was used in order to do this with a high degree of accuracy. Crime linking has the potential to benefit law enforcement greatly because if multiple crimes can be linked together in a reliable way, then the information from the investigation of the separate crimes can be combined which allows for further conclusions, a greater amount of investigation methods, and the efforts to solve each separate crime can be joined together. Because crime linking can be an invaluable asset when presenting in court as well as guiding analysis, it is necessary that the methods used to link the crimes are effective and that their theoretical assumptions can hold true. Since Bayes' theorem is considered a "revolutionary" method in other disciplines, the authors wanted to see if it would be viable for crime linking as well when it is applied to the behavior of offenders which is why this study was conducted.**

This article states that a strength of using Bayesian reasoning in this discipline is that "Bayesian reasoning provides a coherent framework for handling uncertainty, within which the individual behaviours can be weighed against each other to reach a conditional probability of any particular crimes being linked" (Salol et.al., 2012). Bayesian reasoning has been used before in other studies with

*empirical Bayesian*approaches which did not yield significant results, but this study used a

*fully Bayesian approach*which hadn't been done before.

In this study, 116 homicides belonging to 19 separate series of homicides were analyzed in regards to number of victims, time period in which the homicides occurred, age of the offender, etc. Basically all of the details of the crimes were analyzed using a Bayesian model that assigned different probabilities on observing specific behaviors based on which series that the crime belonged to. The researchers used a leave-one-out cross-validation scenario (LOOCV) in order to test the model. When the results of the scenario were analyzed, it was found that the Bayesian model correctly classified 83.6% of the cases. All in all, the researchers seemed to be very impressed with the accuracy of the Bayesian model and feel that it is a promising method to use for crime linkage as it can distinguish even minor behavior variations and is useful in the beginning of the series which is very important.

**Critique:**

**I thought this article did a very good job of explaining how useful and accurate the Bayesian model can be when it comes to linking crimes, but it did not go into very much detail about how exactly the researchers went about doing this. It did not actually show the model or explain how it worked which I think would have been beneficial. Other than that, however, the article did a pretty good job of critiquing itself as it mentioned all the limitations of the study in the discussion section. Overall, I found this article to be extremely interesting because it delves more into law enforcement intelligence and crime analysis perspectives which I appreciated.**

Source:

http://content.ebscohost.com/ContentServer.asp?T=P&P=AN&K=89989477&S=R&D=a9h&EbscoContent=dGJyMMTo50SeprA4zdnyOLCmr02eqLBSsaq4SLSWxWXS&ContentCustomer=dGJyMPGstFGwprVLuePfgeyx43zx

For some reason I couldn’t access the document you posted. Nonetheless, what is the difference between empirical Bayesian and a fully Bayesian approach? And what was the key difference between the two that made the latter technique successful in this study?

ReplyDeleteUnfortunately this article did not go too in depth with this. All it said was that an empirical approach is unable to coherently deal with the

Deleteuncertainty of unknown prior probabilities and in a fully Bayesian approach, the marginal data likelihoods can appropriately take into

account the presence of missing data concerning some behaviors and cases.

You mentioned the researchers were very impressed with the Bayseian approach. Moreover, from your summary, I gathered that the Bayesisan Analysis is highly statistical and computational. Do you, however, believe that cognitive biases can still skew the data at any point of the highly iterative process?

ReplyDeleteBayes analysis technique generally works depending upon a given value; then, with mathematical calculations one can find an updated estimate according to that previous given estimate. But, in this study the researchers knew everything and they, technically, didn't produce an estimate. And probably since everything were known to them there might be massive hindsight bias in this study.

ReplyDeleteThat is an interesting perspective. I overlooked that point.

ReplyDeleteI too found this an interesting read from a law enforcement perspective. It seems like this technique worked in identifying which series each homicide belonged to, but I'm curious on whether it also could possibly identify when a homicide or crime does not belong or link to a series? It is extremely beneficial to be able to identify when crimes are committed by the same offender, but it is just as important to be able to identify when they are an isolated incident.

ReplyDeleteI would be interested to see if this approach would be able to identify if a crime does not link to the series either. Since this study compared known series of crimes against one another and identified them pretty accurately, I would assume to some degree that it could distinguish when a crime does not belong to a series. However, since all of the incidents did belong to a series and there were no isolated incidents in this study, I think that more research would have to be done to answer this question.

DeleteI didn't think about Bayes from a law enforcement point of view, but it makes sense. The idea of gradually building evidence like you build a criminal case would seem to mesh well with Bayes.

ReplyDeleteI never did either until I came across this article. I thought it was a really interesting way to apply this technique.

Delete