In this paper, we propose a unifying treatment of several strategies for training mixture models from label-deficient data. After a review of different approaches to estimating classification models on partially labelled data using mixture models, we identify a number of problems which lead us to propose a new EM variant. The aim is to better handle unlabelled data and provide a more confident ...

In this paper we consider sufficient conditions in order to stochastically compare random vectors of multivariate mixture models. In particular we consider stochastic and convex orders, the likelihood ratio order, and the hazard rate and mean residual life dynamic orders. Applications to proportional hazard models and mixture models in risk theory are also given. AMS Subject Classification: 60E...

\Ve a approach to measures constrained to lie in a convex set. \Ve measures as mixtures known extreme measures, and so the of eS1Jlnlal;lng a constrained measure becomes one of estimating an unconstrained mlXmeasure. constraints arise in many such as estimation of the mean estimation under stochastic constraints. Vve describe mixture representation techniques for these and other and to maximum ...

Twitter offers an unprecedented advantage on live reporting of the events happening around the world. However, summarizing the Twitter event has been a challenging task that was not fully explored in the past. In this paper, we propose a participant-based event summarization approach that “zooms-in” the Twitter event streams to the participant level, detects the important sub-events associated ...

Machine failure can have a significant impact on the throughput of manufacturing systems, therefore accurate modelling of breakdowns in manufacturing simulation models is essential. Finite mixture distributions have been successfully used by Ford Motor Company to model machine breakdown durations in simulation models of engine assembly lines. These models can be very complex, with a large numbe...

This paper discusses the use of a secondary likelihood classifier scheme for improving speaker recognition performance. The system models the output likelihoods of a typical Gaussian Mixture Model system across multiple speakers. The Output Probability Distributions (OPD) of the primary classifiers contain information on inter-speaker relationships, and are modelled by secondary classifiers to ...

In this paper we discuss implementing Bayesian fully nonparametric regression by defining a process prior on distributions which depend on covariates. We consider the problem of centring our process over a class of regression models and propose fully nonparametric regression models with flexible location structures. We also introduce a non-trivial extension of a dependent finite mixture model p...