The characterization of Monte Carlo errors for the quantification of the value of forensic evidence. Issue 8 (24th May 2017)

• Record Type: Journal Article
• Authors: Ommen, Danica M.; Saunders, Christopher P.; Neumann, Cedric
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 8(2017)
• Page Start: 1608
• Page End: 1643
• Publication Date: 2017-05-24
• Subjects: Bayes factor -- forensic science -- Monte Carlo -- standard error -- Gibbs sampling -- Bayesian -- model selection; 62F15 -- 62P99 -- 65C05 -- 65C60; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1280036
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Recent developments in forensic science have lead to a proliferation of methods for quantifying the probative value of evidence by constructing a Bayes Factor that allows a decision-maker to select between the prosecution and defense models. Unfortunately, the analytical form of a Bayes Factor is often computationally intractable. A typical approach in statistics uses Monte Carlo integration to numerically approximate the marginal likelihoods composing the Bayes Factor. This article focuses on developing a generally applicable method for characterizing the numerical error associated with Monte Carlo integration techniques used in constructing the Bayes Factor. The derivation of an asymptotic Monte Carlo standard error (MCSE) for the Bayes Factor will be presented and its applicability to quantifying the value of evidence will be explored using a simulation-based example involving a benchmark data set. The simulation will also explore the effect of prior choice on the Bayes Factor approximations and corresponding MCSEs.