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Using the truncated normal distribution for Bayes factors in hierarchical model selection.

Abstract Number:

3072

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Poster

Participants:

Dylan Borchert (1), Semhar Michael (1), Christopher Saunders (1)

Institutions:

(1) South Dakota State University, N/A

Co-Author(s):

Semhar Michael
South Dakota State University

Christopher Saunders
South Dakota State University

First Author:

Dylan Borchert
South Dakota State University

Presenting Author:

Dylan Borchert
South Dakota State University

Abstract Text:

In the identification of source problems within forensic science, the forensic examiner is tasked with providing a summary of evidence to allow a decision maker to evaluate the source of some evidence. The type of data encountered in the forensic identification of source problems often has a hierarchical structure, where there is a within and between source distribution for each object in a sample. One method of providing this summary of evidence is through a likelihood ratio (LR) or a Bayes factor (BF). With these methods, it is often the case that the two densities are estimated separately and then the ratio is reported, which can lead to instances where the resulting LR is large due to a small density in the denominator. In this work, we explore the use of the truncated normal distribution for use in LRs and BFs to attempt to alleviate this phenomenon. We also begin to characterize the robustness of these truncated normal LR methods.

Keywords:

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Variable/model selection

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