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- Publisher Website: 10.1093/biomet/asx081
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Article: Convergence of regression-adjusted approximate Bayesian computation
Title | Convergence of regression-adjusted approximate Bayesian computation |
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Authors | |
Keywords | Partial information Approximate Bayesian computation Importance sampling Local-linear regression |
Issue Date | 2018 |
Citation | Biometrika, 2018, v. 105, n. 2, p. 301-318 How to Cite? |
Abstract | © 2018 Biometrika Trust. We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced byBeaumont et al. (2002). We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data sample size increases. This compares favourably to results for standard approximate Bayesian computation, where the only way to obtain a posterior that correctly quantifies uncertainty is to choose a much smaller bandwidth, one for which the acceptance probability tends to zero and hence for which Monte Carlo error will dominate. |
Persistent Identifier | http://hdl.handle.net/10722/267094 |
ISSN | 2023 Impact Factor: 2.4 2023 SCImago Journal Rankings: 3.358 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Wentao | - |
dc.contributor.author | Fearnhead, Paul | - |
dc.date.accessioned | 2019-01-31T07:20:30Z | - |
dc.date.available | 2019-01-31T07:20:30Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Biometrika, 2018, v. 105, n. 2, p. 301-318 | - |
dc.identifier.issn | 0006-3444 | - |
dc.identifier.uri | http://hdl.handle.net/10722/267094 | - |
dc.description.abstract | © 2018 Biometrika Trust. We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced byBeaumont et al. (2002). We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data sample size increases. This compares favourably to results for standard approximate Bayesian computation, where the only way to obtain a posterior that correctly quantifies uncertainty is to choose a much smaller bandwidth, one for which the acceptance probability tends to zero and hence for which Monte Carlo error will dominate. | - |
dc.language | eng | - |
dc.relation.ispartof | Biometrika | - |
dc.subject | Partial information | - |
dc.subject | Approximate Bayesian computation | - |
dc.subject | Importance sampling | - |
dc.subject | Local-linear regression | - |
dc.title | Convergence of regression-adjusted approximate Bayesian computation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1093/biomet/asx081 | - |
dc.identifier.scopus | eid_2-s2.0-85048666946 | - |
dc.identifier.volume | 105 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 301 | - |
dc.identifier.epage | 318 | - |
dc.identifier.eissn | 1464-3510 | - |
dc.identifier.isi | WOS:000434111200004 | - |
dc.identifier.issnl | 0006-3444 | - |