Using Infer.NET for Statistical Analyses.
We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family of deterministic approximation algorithms rather...
| Publicado en: | American Statistician Vol. 65; no. 2; pp. 115 - 127 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Statistical Association
May 2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508430720&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508430720 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: N pubinfo: dt: May 2011 vid: 65 iid: 2 pid: 543 pub: American Statistical Association artinfo: ui: 508430720 10.1198/tast.2011.10169 ppf: 115 ppct: 12 formats: tig: atl: Using Infer.NET for Statistical Analyses. aug: au: Wang, S. S. J. Wand, M. P. su: Bayesian analysis Probability theory sug: subj: Bayesian analysis Probability theory ab: We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the well-known BUGS Bayesian inference packages in that its main engine is the variational Bayes family of deterministic approximation algorithms rather than Markov chain Monte Carlo. The underlying rationale is that such deterministic algorithms can handle bigger problems due to their increased speed, despite some loss of accuracy. We find that Infer.NET is a well-designed computational framework and offers significant speed advantages over BUGS. Nevertheless, the current release is limited in terms of the breadth of models it can handle, and its inference is sometimes inaccurate. Supplemental materials accompany the online version of this article. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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