Target classification with imprecise likelihoods: Mahler's approach

In our recent paper [1] we demonstrated that the standard Bayes classifier, when applied to a problem characterised by imprecise likelihood functions, produces results which are inconsistent with our intuition. As a more appropriate alternative to the standard Bayes classifier, we proposed in [1] a classification method based on the transferrable belief model (TBM). Mahler [2, ch. 4-8] recently proposed a novel approach to Bayesian estimation, fusion, and classification, applicable to situations where the information (priors, measurements, likelihoods) is imprecise and vague in addition to being random. The purpose of this letter is to demonstrate that Mahler's approach can produce identical results to those obtained using the TBM classifier.