A bivariate finite mixture growth model with selection

Explaining Heterogeneity in Risk Preferences Using a Finite Mixture Model

This paper studies the effect of the space (distance) between lotteries' outcomes on risk-taking behavior and the shape of estimated utility and probability weighting functions. Previously investigated experimental data shows a significant space effect in the gain domain. As compared to low spaced lotteries, high spaced lotteries are associated with higher risk aversion for high probabilities o...

Image Segmentation Method Based On Finite Doubly Truncated Bivariate Gaussian Mixture Model with Hierarchical Clustering

Image segmentation is one of the most important area of image retrieval. In colour image segmentation the feature vector of each image region is ’n’ dimension different from grey level image. In this paper a new image segmentation algorithm is developed and analyzed using the finite mixture of doubly truncated bivariate Gaussian distribution by integrating with the hierarchical clustering. The ...

Bayesian variable selection for finite mixture model of linear regressions

We propose a Bayesian method for variable selection in the finite mixture model of linear regressions. The model assumes that the observations come from a heterogeneous population which is a mixture of a finite number of sub-populations. Within each sub-population, the response variable can be explained by a linear regression on the predictor variables. So the whole data set can be modeled by a...

On Model Selection and Concavity for Finite Mixture Models

Variational approximations in Bayesian model selection for finite mixture distributions

Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analysis of mixtures of Gaussians. We also consider how the Deviance Information Criterion, or DIC, devised by Spiegelhalter et al. (2002), can be extended to these types of model by exploiting the use of variational approxi...