The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in nonparametric hierarchical mixture models and point out how possible computational issues can be successfully addressed. To this end, we first provide a concise and accessible introduction to normalized random measures with independent increments. Then, we explain in detail a particular way of sampling from the posterior using the Ferguson-Klass representation. We develop a thorough comparative analysis for location-scale mixtures that considers a set of alternatives for the mixture kernel and for the nonparametric component. Simulation results indicate that normalized random measure mixtures potentially represent a valid default choice for density estimation problems. As a byproduct of this study an R package to fit these models was produced and is available in the Comprehensive R Archive Network (CRAN).Comment: Published in at http://dx.doi.org/10.1214/13-STS416 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

This paper generalizes the discrete time independent increment beta process of Hjort (1990), for modelling discrete failure times, and also generalizes the independent gamma process for modelling piecewise constant hazard rates (Walker and Mallick, 1997). The generalizations are from independent increment to Markov increment prior processes allowing the modelling of smoothness. We derive posterior distributions and undertake a full Bayesian analysis.

This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process.Comment: Published at http://dx.doi.org/10.1214/009053604000000625 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.