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Embarrassingly Parallel Sequential Markov-chain Monte Carlo for Large Sets of Time Series

Leisen, Fabrizio, Craiu, Radu, Casarin, Roberto (2016) Embarrassingly Parallel Sequential Markov-chain Monte Carlo for Large Sets of Time Series. Statistics and its interface, 9 (4). pp. 497-508. ISSN 1938-7989. E-ISSN 1938-7997. (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:52555)

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.
Official URL:
http://dx.doi.org/10.4310/SII.2016.v9.n4.a9

Abstract

Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms.

In the case of massive data sets, running the Metropolis-Hastings sampler

to draw from the posterior distribution becomes prohibitive due to the large number

of likelihood terms that need to be calculated at each iteration. In order to perform

Bayesian inference for a large set of time series, we consider an algorithm that combines

“divide and conquer” ideas previously used to design MCMC algorithms for big data

with a sequential MCMC strategy. The performance of the method is illustrated using

a large set of financial data.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Fabrizio Leisen
Date Deposited: 30 Nov 2015 18:11 UTC
Last Modified: 17 Aug 2022 12:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/52555 (The current URI for this page, for reference purposes)
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
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