title: Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data.
abstract: As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches--with the help of Bayesian networks--for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).
authors.affiliation: The University of Lausanne, Ecole des Sciences Criminelles, Institut de Police, Scientifique, le Batochime, 1015 Lausanne-Dorigny, Switzerland. alex.biedermann@unil.ch
authors.forename: A
authors.initials: A
authors.lastname: Biedermann
authors.forename: F
authors.initials: F
authors.lastname: Taroni
authors.forename: S
authors.initials: S
authors.lastname: Bozza
mesh_terms: D001499:Bayes Theorem; D044707:Forensic Sciences; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D035501:Uncertainty