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An adaptive truncation method for inference in Bayesian nonparametric models

Griffin, Jim E. (2014) An adaptive truncation method for inference in Bayesian nonparametric models. Statistics and Computing, 26 (1). pp. 423-441. ISSN 0960-3174. (doi:10.1007/s11222-014-9519-4) (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) (KAR id:60589)

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/10.1007/s11222-014-9519-4

Abstract

Many exact Markov chain Monte Carlo algorithms have been developed for posterior inference in Bayesian nonparametric models which involve infinite-dimensional priors. However, these methods are not generic and special methodology must be developed for different classes of prior or different models. Alternatively, the infinite-dimensional prior can be truncated and standard Markov chain Monte Carlo methods used for inference. However, the error in approximating the infinite-dimensional posterior can be hard to control for many models. This paper describes an adaptive truncation method which allows the level of the truncation to be decided by the algorithm and so can avoid large errors in approximating the posterior. A sequence of truncated priors is constructed which are sampled using Markov chain Monte Carlo methods embedded in a sequential Monte Carlo algorithm. Implementational details for infinite mixture models with stick-breaking priors and normalized random measures with independent increments priors are discussed. The methodology is illustrated on infinite mixture models, a semiparametric linear mixed model and a nonparametric time series model.

Item Type: Article
DOI/Identification number: 10.1007/s11222-014-9519-4
Uncontrolled keywords: Sequential Monte CarloDirichlet processPoisson–Dirichlet processNormalized random measures with independent incrementsTruncation error Stick-breaking priors
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 28 Feb 2017 11:01 UTC
Last Modified: 16 Feb 2021 13:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60589 (The current URI for this page, for reference purposes)
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
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