Is it possible to crowdsource categorization? Amongst the challenges: (a) each worker has only a partial view of the data, (b) different workers may have different clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how workers may approach clustering and show how one may infer clusters / ...

It has always been a great challenge for clustering algorithms to automatically determine the cluster numbers according to the distribution of datasets. Several approaches have been proposed to address this issue, including the recent promising work which incorporate Bayesian Nonparametrics into the k-means clustering procedure. This approach shows simplicity in implementation and solidity in t...

A numerical framework for clustering of time series via Markov chain Monte Carlo (MCMC) method is presented. It combines concepts from recently introduced variational time series analysis and regularized clustering functional minimization (I. Horenko, SIAM SISC vol. 32(1):62-83 ) with Bayesian approach and MCMC. Conceptual advantage of the presented combined framework is that it allows to addre...

We develop a new Bayesian approach to estimate the parameters of a latent-class model for the joint clustering of both modes of two-mode data matrices. Posterior results are obtained using a Gibbs sampler with data augmentation. Our Bayesian approach has three advantages over existing methods. First, we are able to do statistical inference on the model parameters, which would not be possible us...

Population genetic analyses traditionally focus on the frequencies of alleles or genotypes in 'populations' that are delimited a priori. However, there are potential drawbacks of amalgamating genetic data into such composite attributes of assemblages of specimens: genetic information on individual specimens is lost or submerged as an inherent part of the analysis. A potential also exists for ci...

We present the “Bayesian Occupancy Filter” (BOF) and the “Fast ClusteringTracking” algorithms as a framework for robust sensing and multi-target tracking using multiple sensors. Perceiving of the surrounding physical environment reliably is a major demanding in smart systems requiring a high level of safety such as car driving assistant, autonomous robots, and surveillance. The dynamic environm...

One of the focal points of the modern literature on Bayesian nonparametrics has been the problem of clustering, or partitioning, where each data point is modeled as being associated with one and only one of some collection of groups called clusters or partition blocks. Underlying these Bayesian nonparametric models are a set of interrelated stochastic processes, most notably the Dirichlet proce...

This paper presents a new approach to the unsupervised training of Bayesian network classifiers. Three models have been analysed: the Chow and Liu (CL) multinets; the treeaugmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning. To perform the unsupervised training of these models, the classification maximum likelihood ...

In this paper, we apply Variational Bayesian Estimation and Clustering for speech recognition (VBEC) to an acoustic model adaptation. VBEC can estimate parameter posteriors even when a model includes hidden variables, by using Variational Bayesian approach. In addition, VBEC can select an appropriate model structure in clustering triphone states, according to the amount of available adaptation ...

Methods Nuclear microsatellite variation was assayed in multiple populations (1179 trees, 21 locations, 6–13 locations per species) in Jacaranda copaia (Bignoniaceae), Luehea seemannii (Malvaceae), Simarouba amara (Simaroubaceae) and Symphonia globulifera (Clusiaceae). Population structure was analysed using FST-based statistics and a Bayesian clustering approach (baps). Bayesian coalescent met...