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Related Experiment Video

Updated: Nov 1, 2025

$Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing$

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

Bayesian mixture modelling for glass refractive index measurement.

Zhijian Wen¹, J M Curran¹, S-A Harbison²

¹Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

Science & Justice : Journal of the Forensic Science Society

|June 26, 2021

Summary

Forensic glass analysis uses refractive index measurements to determine evidence weight. A new Dirichlet Process Mixture Model offers a more flexible Bayesian approach for modeling this crucial glass characteristic.

Keywords:

Dirichlet process mixture model Forensic Glass refractive index Probability density estimation

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Area of Science:

Forensic Science
Statistical Modeling
Materials Analysis

Background:

Glass fragments are key physical evidence in forensic investigations.
Statistical methods, particularly Bayesian frameworks, are used to assess the evidential weight of glass characteristics.
Accurate probability distributions of glass properties are essential for robust statistical analysis.

Purpose of the Study:

To introduce and evaluate the Dirichlet Process Mixture Model for analyzing glass refractive index measurements.
To provide a more flexible Bayesian approach for modeling the probability density distribution of refractive index data.

Main Methods:

Application of the Dirichlet Process Mixture Model within a Bayesian methodology.
Modeling of glass refractive index measurements using this novel approach.
Comparison with existing methods like kernel density estimation.

Main Results:

The Dirichlet Process Mixture Model demonstrates flexibility in capturing the underlying distribution of refractive index measurements.
This method allows for a more nuanced assessment of glass characteristics in forensic casework.

Conclusions:

The Dirichlet Process Mixture Model presents a viable and flexible alternative for Bayesian analysis of glass refractive index data.
This approach enhances the statistical treatment of glass evidence in forensic science.