Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice distinct sub-populations do not actually exist. For example, disease s...

The development of high-performance thermal systems has increased interest in heat transfer enhancement techniques. The application of additives to heat transfer liquids is one of the noticeable effort to enhance heat transfer. In this paper two-dimensional unsteady incompressible nanofluid flow in a confined jet at the laminar flow regime is numerically investigated. The Mixture model is consi...

Finite mixture models, that is, weighted averages of parametric distributions, provide a powerful way to extend parametric families of distributions to fit data sets not adequately fit by a single parametric distribution. First-order finite mixture models have been widely used in the physical, chemical, biological, and social sciences for over 100 years. Using maximum likelihood estimation, we ...

background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

Mixture model is a probabilistic model that denotes the presence of subpopulations within an overall population meanwhile finite mixture model is a mixture model with finite-dimensional. In this paper, finite mixture model is applied and the application of Bayesian method to fit finite mixture model is popular and these application is adopt in the present study in order to explore the relations...

II Lincoln Laboratory has investigated the development of a system that can automatically identify the language of a speech utterance. To perform the task of automatic language identification, we have experimented with four approaches: Gaussian mixture model classification; single-language phone recognition followed by language modeling (PRLM); parallel PRLM, which uses multiple single-language...

In this paper, we present a hybrid speech recognizer combining Hidden Markov Models (HMMs) and a polynomial classifier. In our approach the emission probabilities are not modeled as a mixture of Gaussians but are calculated by the polynomial classifier. However, we do not apply the classifier directly to the feature vector but we make use of the density values of Gaussians clustering the featur...

Aiming at building a dialectal Chinese speech recognizer from a standard Chinese speech recognizer with a small amount of dialectal Chinese speech, a novel, simple but effective acoustic modeling method, named statedependent phoneme-based model merging (SDPBMM) method, is proposed and evaluated, where a tied-state of standard triphone(s) will be merged with a state of the dialectal monophone th...