Making Recursive Bayesian Inference Accessible.
Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive B...
| Publicado en: | American Statistician Vol. 75; no. 2; pp. 185 - 195 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
May2021
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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=150252812&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 150252812 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2021 vid: 75 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 150252812 10.1080/00031305.2019.1665584 ppf: 185 ppct: 10 formats: tig: atl: Making Recursive Bayesian Inference Accessible. aug: au: Hooten, Mevin B. Johnson, Devin S. Brost, Brian M. affil: Colorado Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, Fort Collins, CO Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO Department of Statistics, Colorado State University, Fort Collins, CO Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA Fisheries, Seattle, WA su: Markov chain Monte Carlo sug: subj: Markov chain Monte Carlo keyword: Filtering Hierarchical model Iterative forecasting Parallel processing Sequential inference Filtering Hierarchical model Iterative forecasting Parallel processing Sequential inference ab: Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive Bayesian methods are: Prior- and Proposal-Recursive Bayes. Prior-Recursive Bayes uses Bayesian updating, fitting models to partitions of data sequentially, and provides a way to accommodate new data as they become available using the posterior from the previous stage as the prior in the new stage based on the latest data. Proposal-Recursive Bayes is intended for use with hierarchical Bayesian models and uses a set of transient priors in first stage independent analyses of the data partitions. The second stage of Proposal-Recursive Bayes uses the posteriors from the first stage as proposals in a Markov chain Monte Carlo algorithm to fit the full model. We combine Prior- and Proposal-Recursive concepts to fit any Bayesian model, and often with computational improvements. We demonstrate our method with two case studies. Our approach has implications for big data, streaming data, and optimal adaptive design situations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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