Score-based approaches for computing forensic likelihood ratios are becoming more prevalent in the forensic literature. When two items of evidential value are entangled via a scorefunction, several nuances arise when attempting to model the score behavior under the competing source-level propositions. Specific assumptions must be made in order to appropriately model the numerator and denominato...

Correlated noise is generally present in experimentally recorded electron energy loss spectra due to a non-ideal electron detector. In this contribution we describe a method to experimentally measure the noise properties of the detector as well as the consequences it has for model-based quantification using maximum likelihood. The effect of the correlated noise on the maximum likelihood fitting...

Detecting a transient signal of unknown arrival time in noise is actually a binary hypothesis test problem, where the null hypothesis (noise only) is a simple one, while the alternative hypothesis is composite. The generalized likelihood ratio test (GLRT) is a common tool to solve such problems. In this paper we show how order statistics (OS) approach can be used to solve the same problem. We s...

Synthetic aperture radar (SAR) images due to the usage of coherent imaging systems are affected by speckle. So lots of despeckling filters have been introduced up to now to suppress the speckle. Hence, objective and subjective evaluation of the denoised SAR images becomes a necessity. Thereby lots of objective evaluating estimators are introduced to evaluate the performance of despeckling filte...

Likelihood ratio tests are intuitively appealing. Nevertheless, a number of examples are known in which they perform very poorly. The present paper discusses a large class of situations in which this is the case, and analyzes just how intuition misleads us; it also presents an alternative approach which in these situations is optimal. 1. The popularity of likelihood ratio tests Faced with a new...

In this work, we introduce an information-theoretic based correction term to the likelihood ratio classification method for multiple classes. Under certain conditions, the term is sufficient for optimally correcting the difference between the true and estimated likelihood ratio, and we analyze this in the Gaussian case. We find that the new correction term significantly improves the classificat...

Many statistical hypotheses can be formulated in terms of polynomial equalities and inequalities in the unknown parameters and thus correspond to semi-algebraic subsets of the parameter space. We consider large sample asymptotics for the likelihood ratio test of such hypotheses in models that satisfy standard probabilistic regularity conditions. We show that the assumptions of Chernoff's theore...

We link the reverse KL divergence with adversarial learning. This insight enables learning to synthesize realistic samples in two settings: (i) Given a set of samples from the true distribution, an adversarially learned likelihood-ratio and a new entropy bound are used to learn a GAN model, that improves synthesized sample quality relative to previous GAN variants. (ii) Given an unnormalized di...