Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and set of explanatory variables from several unknown latent homogeneous groups. However, classical EM algorithm Gibbs sampling deal with this model have weak points. In paper, non-iterative for fitting FMNR is proposed Bayesian perspective. The procedure can generate inde...

The application of finite mixture regression models has recently gained an interest from highway safety researchers because of its considerable potential for addressing unobserved heterogeneity. Finite mixture models assume that the observations of a sample arise from two or more unobserved components with unknown proportions. Both fixed and varying weight parameter models have been shown to be...

Finite mixture models can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex representation and are not suitable for interpretation purposes as such. In this paper, we present a methodology to describe the finite mixture of multivariate Bernoulli distributions with a compact and understandable description. Fi...

A hierarchical extension of the finite mixture model is presented that can be used for the analysis of nested data structures. The model permits a simultaneous model-based clustering of lowerand higher-level units. Lower-level observations within higher-level units are assumed to be mutually independent given cluster membership of the higher-level units. The proposed model can be seen as a fini...

An adaptive spatial Gaussian mixture model is proposed for clustering based color image segmentation. A new clustering objective function which incorporates the spatial information is introduced in the Bayesian framework. The weighting parameter for controlling the importance of spatial information is made adaptive to the image content to augment the smoothness towards piecewisehomogeneous regi...

in the present study, computational fluid dynamics (cfd) techniques and artificial neural networks (ann) are used to predict the pressure drop value (δp ) of al2o3-water nanofluid in flat tubes. δp  is predicted taking into account five input variables: tube flattening (h), inlet volumetric flow rate (qi  ), wall heat flux (qnw  ), nanoparticle volume fraction (φ) and nanoparticle diameter (dp ...

Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis and so on. Model-based clustering is one of the most important and widely used clustering methods. This paper presents ...

The Dirichlet process is a prior used in nonparametric Bayesian models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as...

This paper considers a new mixture of time homogeneous finite Markov chains where the mixing is on the rate of movement and develops the EM algorithm for the maximum likelihood estimation of the parameters of the mixture. A continuous and discrete time versions of the mixture are defined and their estimation is considered separately. The simulation study is carried out for the continuous time m...