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dc.contributor.authorCao, Jian
dc.contributor.authorDurante, Daniele
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2022-04-12T08:52:23Z
dc.date.available2022-04-12T08:52:23Z
dc.date.issued2021-01-19
dc.identifier.urihttp://hdl.handle.net/10754/676230
dc.description.abstractScalable computation of predictive probabilities in probit models with Gaussian process priors
dc.publisherGithub
dc.relation.urlhttps://github.com/danieledurante/PredProbitGP
dc.titledanieledurante/PredProbitGP: Scalable computation of predictive probabilities in probit models with Gaussian process priors
dc.typeSoftware
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.institutionDepartment of Decision Sciences and Bocconi Institute for Data Science and Analytics, Bocconi University, Milano, Italy
kaust.personCao, Jian
kaust.personGenton, Marc G.
dc.relation.issupplementtoDOI:10.1080/10618600.2022.2036614
display.relations<b>Is Supplement To:</b><br/> <ul><li><i>[Article]</i> <br/> Cao, J., Durante, D., & Genton, M. G. (2022). Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors. Journal of Computational and Graphical Statistics, 1–12. https://doi.org/10.1080/10618600.2022.2036614. DOI: <a href="https://doi.org/10.1080/10618600.2022.2036614" >10.1080/10618600.2022.2036614</a> Handle: <a href="http://hdl.handle.net/10754/665127" >10754/665127</a></a></li></ul>
dc.identifier.githubdanieledurante/PredProbitGP


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