This article proposes a method for approximating integrated likelihoods in finite mixture models. We formulate the model in terms of the unobserved group memberships, z, and make them the variables of integration. The integral is then evaluated using importance sampling over the z. We propose an adaptive importance sampling function which is itself a mixture, with two types of component distrib...

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...

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 generative model is, of necessity, a vast simplification of the deeply complex real-world phenomena that govern any observed data set. It is only via this simplification that we can arrive at a tractable data analysis and discover meaningful and actionable patterns in data. In this sense, typically any model of a real-world data set is misspecified, and misspecification is unavoidable. But wh...

A Bayesian nonparametric method for unimodal densities on the real line is provided by considering a class of species sampling mixture models containing random densities that are unimodal and not necessarily symmetric. This class of densities generalize the model considered by Brunner (1992), in which the Dirichlet process is replaced by a more general class of species sampling models. A novel ...

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...

Testing for homogeneity in finite mixture models has been investigated by many authors. The asymptotic null distribution of the likelihood ratio test (LRT) is very complex and difficult to use in practice. In this paper we propose a modified LRT for homogeneity in finite mixture models with a general parametric kernel distribution family. The modified LRT has a χ2-type null limiting distributio...

I discuss the recommendations and cautions in Steinley and Brusco's (2011) article on the use of finite models to cluster a data set. In their article, much use is made of comparison with the K-means procedure. As noted by researchers for over 30 years, the K-means procedure can be viewed as a special case of finite mixture modeling in which the components are in equal (fixed) proportions and a...

Bivariate Poisson regression models for ratemaking in car insurance has been previously used. They included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. These models are now revisited in order to consider alternatives. A 2-finite mixture of bivariate Poisson regression models is used to demonstrate that the overdispersion in the data requires m...

Image segmentation is an important problem which addresses the needs of lots of biomedical applications. In this work, we adress the problem with MRF-coupled mixture models. In the standard finite mixture models, the number of segments that we are supposed to find in an image is fixed. With the Bayesian non-parametrics formulation we automatically find the number of segments by considering an i...