Skip to main content

Generalized Species Sampling Priors with Latent Beta reinforcements

Leisen, Fabrizio, Costa, Thiago, Airoldi, Edoardo, Bassetti, Federico, Guindani, Michele (2014) Generalized Species Sampling Priors with Latent Beta reinforcements. Journal of the American Statistical Association, 109 (508). pp. 1466-1480. ISSN 0162-1459. (doi:10.1080/01621459.2014.950735) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1080/01621459.2014.950735

Abstract

Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data.

Item Type: Article
DOI/Identification number: 10.1080/01621459.2014.950735
Uncontrolled keywords: Bayesian nonparametrics, Cancer, Genomics, MCMC, Predictive probability functions, Random partitions
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 04 Oct 2014 07:58 UTC
Last Modified: 01 Aug 2019 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/43181 (The current URI for this page, for reference purposes)
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
  • Depositors only (login required):