Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8547
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dc.contributor.authorallMarzocchi, W.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italiaen
dc.contributor.authorallZechar, J.; ETH Zurichen
dc.contributor.authorallJordan, T.; University of Southern Californiaen
dc.date.accessioned2013-03-21T08:06:13Zen
dc.date.available2013-03-21T08:06:13Zen
dc.date.issued2012en
dc.identifier.urihttp://hdl.handle.net/2122/8547en
dc.description.abstractThe assessment of earthquake forecast models for practical purposes requires more than simply checking model consistency in a statistical framework. One also needs to understand how to construct the best model for specific forecasting applications. We describe a Bayesian approach to evaluating earthquake forecasting models, and we consider related procedures for constructing ensemble forecasts. We show how evaluations based on Bayes factors, which measure the relative skill among forecasts, can be complementary to common goodness-of-fit tests used to measure the absolute consistency of forecasts with data. To construct ensemble forecasts, we consider averages across a forecast set, weighted by either posterior probabilities or inverse log- likelihoods derived during prospective earthquake forecasting experiments. We account for model correlations by conditioning weights using the Garthwaite–Mubwandarikwa capped eigenvalue scheme. We apply these methods to the Regional Earthquake Like- lihood Models (RELM) five-year earthquake forecast experiment in California, and we discuss how this approach can be generalized to other ensemble forecasting applications. Specific applications of seismological importance include experiments being conducted within the Collaboratory for the Study of Earthquake Predictability (CSEP) and ensemble methods for operational earthquake forecasting.en
dc.language.isoEnglishen
dc.publisher.nameSeismological Society of Americaen
dc.relation.ispartofBulletin of the Seismological Society of Americaen
dc.relation.ispartofseries/102(2012)en
dc.subjectearthquake forecastingen
dc.subjectensemble modelen
dc.titleBayesian Forecast Evaluation and Ensemble Earthquake Forecastingen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber2574 – 2584en
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.02. Earthquake interactions and probabilityen
dc.identifier.doi10.1785/0120110327en
dc.description.obiettivoSpecifico4.2. TTC - Modelli per la stima della pericolosità sismica a scala nazionaleen
dc.description.journalTypeJCR Journalen
dc.description.fulltextrestricteden
dc.relation.issn0037-1106en
dc.relation.eissn1943-3573en
dc.contributor.authorMarzocchi, W.en
dc.contributor.authorZechar, J.en
dc.contributor.authorJordan, T.en
dc.contributor.departmentIstituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italiaen
dc.contributor.departmentETH Zurichen
dc.contributor.departmentUniversity of Southern Californiaen
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptETH Zurich-
crisitem.author.deptUniv. of Southern California, USA-
crisitem.author.orcid0000-0002-9114-1516-
crisitem.classification.parent04. Solid Earth-
crisitem.department.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
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