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Prediction of the distribution of thepercentages of narcotic substances in drug seizures
Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Filosofiska fakulteten. Swedish Police Auhtority, National Forensic Centre (NFC).ORCID-id: 0000-0001-9385-5443
School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom.
2016 (Engelska)Konferensbidrag, Enbart muntlig presentation (Övrigt vetenskapligt)
Abstract [en]

The percentage of the narcotic substance in a drug seizure may vary a lot depending on when and from whom the seizure was taken. Seizures from a typical consumer would in general show low percentages, while seizures from the early stages of a drug dealing chain would show higher percentages (these will be diluted). Historical records from the determination of the percentage of narcotic substance in seized drugs reveal that the mean percentage but also the variation of the percentage can differ between years. Some drugs show close to monotonic trends while others are more irregular in the temporal variation.

Legal fact finders must have an up-to-date picture of what is an expected level of the percentage and what levels are to be treated as unusually low or unusually high. This is important for the determination of the sentences to be given in a drug case.

In this work we treat the probability distribution of the percentage of a narcotic substance in a seizure from year to year as a time series of functions. The functions are probability density functions of beta distributions, which are successively updated with the use of point mass posteriors for the shape parameters. The predictive distribution for a new year is a weighted sum of beta distributions for the previous years where the weights are found from forward validation. We show that this method of prediction is more accurate than one that uses a predictive distribution built on a likelihood based on all previous years.

Ort, förlag, år, upplaga, sidor
2016.
Nyckelord [en]
predictive distribution, forward validation, percentages
Nationell ämneskategori
Sannolikhetsteori och statistik Juridik och samhälle
Identifikatorer
URN: urn:nbn:se:liu:diva-159537OAI: oai:DiVA.org:liu-159537DiVA, id: diva2:1342037
Konferens
ISBA 2016 World Meeting (International Society for Bayesian Analysis)
Tillgänglig från: 2019-08-12 Skapad: 2019-08-12 Senast uppdaterad: 2019-08-12

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Nordgaard, Anders
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Statistik och maskininlärningFilosofiska fakulteten
Sannolikhetsteori och statistikJuridik och samhälle

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