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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)

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/10.4310/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: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 30 Nov 2015 18:11 UTC
Last Modified: 29 May 2019 16:33 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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