Dependent mixtures of geometric weights priors

Spyridon J. Hatjispyros, Christos Merkatas, Theodoros Nicoleris and Stephen G. Walker

Computational Statistics & Data Analysis, 2018, vol. 119, issue C, 1-18

Abstract: A new approach to the joint estimation of partially exchangeable observations is presented. This is achieved by constructing a model with pairwise dependence between random density functions, each of which is modeled as a mixture of geometric stick breaking processes. The main contention is that mixture modeling with Pairwise Dependent Geometric Stick Breaking Process (PDGSBP) priors is sufficient for prediction and estimation purposes; that is, making the weights more exotic does not actually enlarge the support of the prior. Moreover, the corresponding Gibbs sampler for estimation is faster and easier to implement than the Dirichlet Process counterpart.

Keywords: Bayesian nonparametric inference; Mixture of Dirichlet process; Geometric stick breaking weights; Geometric stick breaking mixtures; Dependent process (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:119:y:2018:i:c:p:1-18

DOI: 10.1016/j.csda.2017.09.006

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