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...

background : so far, several studies were conducted to estimate the prevalence of cigarette smoking in iran, but none of them used a statistical model to deal with unobserved smokers. the present study planned to estimate the accurate prevalence of cigarette smoking using mixture of truncated poisson distribution. methods : a cross-sectional study was conducted in hamadan, west of iran in 2009,...

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 ...

Statistical Spatial Color Information Modeling in Images and Applications Walid Elguebaly Image processing, among its vast applications, has proven particular efficiency in quality control systems. Quality control systems such as the ones in the food industry, fruits and meat industries, pharmaceutic, and hardness testing are highly dependent on the accuracy of the algorithms used to extract im...

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the...