Microarrays have become increasingly common in biological and medical research. They enable the simultaneous study of thousands of genes and provide gene expression information on a whole genome level. A major goal of microarray experiments is to determine which genes are differentially expressed between samples. A mixed model approach using the Johnson’s system of distributions and Baye’s form...

We develop a general method to match unstructured text reviews to a structured list of objects. For this, we propose a language model for generating reviews that incorporates a description of objects and a generic review language model. This mixture model gives us a principled method to find, given a review, the object most likely to be the topic of the review. Extensive experiments and analysi...

This work proposes a contribution aiming at probabilistic model estimation, in the setting of distributed, decentralized, data-sharing computer systems. Such systems are developing over the internet, and also exist as sensor networks, for instance. Our general goal consists in estimating a probability distribution over a data set which is distributed into subsets located on the nodes of a distr...

This paper describes a method for automatic singer identification from polyphonic musical audio signals including sounds of various instruments. Because singing voices play an important role in musical pieces with a vocal part, the identification of singer names is useful for music information retrieval systems. The main problem in automatically identifying singers is the negative influences ca...

Dynamic events comprise of spatiotemporal atomic units. In this paper we model them using a mixture model. Events are represented using a framework based on the Mixture of Factor Analyzers (MFA) model. It is to be noted that our framework is generic and is applicable for any mixture modelling scheme. The MFA, used to demonstrate the novelty of our approach, clusters events into spatially cohere...

Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model base...

Some Recent Advances in Nonand Semiparametric Bayesian Modeling with Copulas, Mixtures, and Latent Variables by Jared S. Murray Department of Statistical Science Duke University

The efficiency of two Bayesian order estimators is studied. By using nonparametric techniques, we prove new underestimation and overestimation bounds. The results apply to various models, including mixture models. In this case, the errors are shown to be O(e −an) and O((log n) b / √ n) (a, b > 0), respectively. 1. Introduction. Order identification deals with the estimation and test of a struct...

Latent class and the Grade of Membership (GoM) models are two examples of latent structure models. Latent class models are discrete mixture models. The GoM model has been originally developed as an extension of latent class models to a continuous mixture. This note describes a constrained latent class model which is equivalent to the GoM model, and provides a detailed proof of this equivalence....

In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of an existing probabilistic generative model: Parametric Mixture Model(PMM) by hierarchical Bayes model. PMM models multiple-topic documents by mixing model parameters of each single topic with an equal mixture ratio. P...