A composite Bayesian hierarchical model of compositional data with zeros

Gary Napier, Tereza Neocleous, Agostino Nobile

Research output: Contribution to journalArticlepeer-review


We present an effective approach for modelling compositional data with large concentrations of zeros and several levels of variation, applied to a database of elemental compositions of forensic glass of various use types. The procedure consists of the following: (i) partitioning the data set in subsets characterised by the same pattern of presence/absence of chemical elements and (ii) fitting a Bayesian hierarchical model to the transformed compositions in each data subset. We derive expressions for the posterior predictive probability that newly observed fragments of glass are of a certain use type and for computing the evidential value of glass fragments relating to two competing propositions about their source. The model is assessed using cross-validation, and it performs well in both the classification and evidence evaluation tasks.
Original languageEnglish
Pages (from-to)96 - 108
Number of pages13
JournalJournal of Chemometrics
Issue number2
Early online date13 Oct 2014
Publication statusPublished - Feb 2015

Bibliographical note

Copyright © 2014 John Wiley & Sons, Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.


  • Bayes factor
  • Classification
  • Evidence evaluation
  • Forensic glass
  • Markov chain Monte Carlo

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