Author:
Bozza Silvia,Taroni Franco,Biedermann Alex
Abstract
AbstractThis chapter develops and discusses Bayes factors for investigative purposes, i.e. situations in which no potential source is available for comparison purposes. A typical example for this is the problem of classifying items or individuals into one of several classes or populations on the basis of available data (e.g., measurements of one or more attributes). More specifically, material of interest is analyzed (e.g., the quantity of cocaine present on banknotes) and results are evaluated in terms of their effect on the odds in favor of a proposition according to which the recovered material originates from a given population (e.g., banknotes in general circulation), compared to an alternative proposition according to which the recovered items originate from another population (e.g., banknotes related to drug trafficking). The problem of discrimination between populations is addressed for various types of discrete and continuous data, respectively, including an extension to continuous multivariate data. The examples developed in this chapter involve classification for two or more populations. The assessment of model performance is addressed as well.
Publisher
Springer International Publishing
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