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Deconvolution of Dust Mixtures by Latent Dirichlet Allocation in Forensic Science

NCJ Number
254153
Date Published
2019
Length
11 pages
Author(s)
Madeline Ausdemore; Cedric Neumann
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2014-IJ-CX-K088
Annotation
This article explains a technique for analyzing dust particles on a person's shoe that indicate whether a person has been in the vicinity of a crime scene.
Abstract
Dust particles recovered from the soles of shoes may be indicative of the sites recently visited by an individual and the presence of an individual at a particular site of interest, e.g., the scene of a crime. By describing the dust profile of a given site by a multinomial distribution over a fixed number of dust particle types, investigators can define the probability distribution of the mixture of dust recovered from the sole of a shoe via Latent Dirichlet Allocation. The current study used Variational Bayesian Inference to study the parameters of the model and used their resulting posterior distributions to make inference on (a) the contributions of sites of interest to a dust mixture, and (b) the particle profiles associated with these sites. (publisher abstract modified)
Date Created: July 20, 2021