Ramsés H. Mena

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BACKGROUND Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the(More)
Discrete random probability measures and the exchangeable random partitions they induce are key tools for addressing a variety of estimation and prediction problems in Bayesian inference. Here we focus on the family of Gibbs-type priors, a recent elegant generalization of the Dirichlet and the Pitman-Yor process priors. These random probability measures(More)
Inference for Expressed Sequence Tags (ESTs) data is considered. We focus on evaluating the redundancy of a cDNA library and, more importantly, on comparing different libraries on the basis of their clustering structure. The numerical results we achieve allow us to assess the effect of an error correction procedure for EST data and to study the(More)
Are Gibbs–type priors the most natural generalization of the Dirichlet process? Abstract Discrete random probability measures and the exchangeable random partitions they induce are key tools for addressing a variety of estimation and prediction problems in Bayesian inference. Indeed, many popular nonparametric priors, such as the Dirichlet and the Pitman–(More)
Sponsors Foreword The 8th Workshop on Bayesian Nonparametrics is the first of this series of meetings co-sponsored by the newly thematic Section on Bayesian Nonparametrics (BNP) of the International Society for Bayesian Analysis. The formation of this Section as well as the increasing number of delegates attending BNP workshops are clear evidence of the(More)
In this paper we provide an explicit probability distribution for classification purposes when observations are viewed on the real line and classifications are to be based on numerical order-ings. The classification model is derived from a Bayesian nonparametric mixture of Dirichlet process model; with some modifications. The resulting approach then more(More)
This paper presents an extension of a general parametric class of transitional models of order p. In these models, the conditional distribution of the current observation, given the present and past history, is a mixture of conditional distributions, each of them corresponding to the current observation, given each one of the p-lagged observations. Such(More)
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