Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Public Library of ScienceDate
2016-02-22Citation
10.1371/journal.pone.0149958
PLoS ONE 11.2 (2016): e0149958
ISSN
1932-6203DOI
10.1371/journal.pone.0149958Funded by
JFP recieved funding from "Ministerio de Economia y Competitividad (ES)" (http://www.mineco.gob.es/) through the project "CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Senal de Voz", with grant number TEC2012-37585-C02-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Project
Gobierno de España. TEC2012-37585-C02-01Editor's Version
http://dx.doi.org/10.1371/journal.pone.0149958Subjects
Algorithm; Paint; Accuracy; Calculation; Calibration; Controlled study; Entropy; Kernel method; Mathematical analysis; Probability; TelecomunicacionesNote
Franco-Pedroso J, Ramos D, Gonzalez-Rodriguez J (2016) Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data. PLoS ONE 11(2): e0149958. doi:10.1371/journal.pone.0149958Rights
© 2016 Franco-Pedroso et al.Abstract
In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (C-llr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints.
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Google Scholar:Franco-Pedroso, Javier
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Ramos Castro, Daniel
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González Rodríguez, Joaquín
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