NCJ Number
252364
Date Published
January 2017
Length
36 pages
Annotation
This article reports on the development of a generally applicable method for characterizing the numerical error associated with Monte Carlo integration techniques used in constructing the Bayes Factor.
Abstract
Recent developments in forensic science have precipitated 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. The current article describes the derivation of an asymptotic Monte Carlo standard error (MCSE) for the Bayes Factor, and its applicability to quantifying the value of evidence is explored, using a simulation-based example involving a benchmark data set. The simulation also explores the effect of prior choice on the Bayes Factor approximations and corresponding MCSEs. (Publisher abstract modified)
Date Published: January 1, 2017
Downloads
Similar Publications
- Community Court Grows in Brooklyn: A Comprehensive Evaluation of the Red Hook Community Justice Center, Final Report
- Natural Variation in Genome Architecture Among 205 Drosophila Melanogaster Genetic Reference Panel Lines
- HFITS: An Analysis Tool for Calculating Heat Flux to Planar Surfaces Using Infrared Thermography