{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:14:10Z","timestamp":1753600450650},"reference-count":21,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2004,12,27]],"date-time":"2004-12-27T00:00:00Z","timestamp":1104105600000},"content-version":"vor","delay-in-days":26,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2004,12]]},"abstract":"<jats:p>A method for combining multiple expert opinions that are encoded in a Bayesian Belief Network (BBN) model is presented and applied to a problem involving the cleanup of hazardous chemicals at a site with contaminated groundwater. The method uses Bayes Rule to update each expert model with the observed evidence, then uses it again to compute posterior probability weights for each model. The weights reflect the consistency of each model with the observed evidence, allowing the aggregate model to be tailored to the particular conditions observed in the site\u2010specific application of the risk model. The Bayesian update is easy to implement, since the likelihood for the set of evidence (observations for selected nodes of the BBN model) is readily computed by sequential execution of the BBN model. The method is demonstrated using a simple pedagogical example and subsequently applied to a groundwater contamination problem using an expert\u2010knowledge BBN model. The BBN model in this application predicts the probability that reductive dechlorination of the contaminant trichlorethene (TCE) is occurring at a site\u2014a critical step in the demonstration of the feasibility of monitored natural attenuation for site cleanup\u2014given information on 14 measurable antecedent and descendant conditions. The predictions for the BBN models for 21 experts are weighted and aggregated using examples of hypothetical and actual site data. The method allows more weight for those expert models that are more reflective of the site conditions, and is shown to yield an aggregate prediction that differs from that of simple model averaging in a potentially significant manner.<\/jats:p>","DOI":"10.1111\/j.0272-4332.2004.00547.x","type":"journal-article","created":{"date-parts":[[2004,12,28]],"date-time":"2004-12-28T06:49:36Z","timestamp":1104216576000},"page":"1529-1538","source":"Crossref","is-referenced-by-count":25,"title":["Site\u2010Specific Updating and Aggregation of Bayesian Belief Network Models for Multiple Experts"],"prefix":"10.1111","volume":"24","author":[{"given":"Neil A.","family":"Stiber","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchell J.","family":"Small","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina","family":"Pantazidou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2004,12,27]]},"reference":[{"key":"e_1_2_6_2_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.44.8.1049"},{"key":"e_1_2_6_3_2","volume-title":"Rational Choice in an Uncertain World","author":"Dawes R. 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Washington DC : OSWER Directive 9200.4\u201017P US Environmental Protection Agency Office of Solid Waste and Emergency Response April 21."},{"key":"e_1_2_6_19_2","volume-title":"Natural Attenuation for Groundwater Remediation","author":"National Research Council","year":"2000"},{"key":"e_1_2_6_20_2","volume-title":"NeticaTM Application for Belief Networks and Influence Diagrams: User's Guide","author":"Norsys Software Corp","year":"1998"},{"key":"e_1_2_6_21_2","doi-asserted-by":"publisher","DOI":"10.1021\/es970105a"},{"key":"e_1_2_6_22_2","doi-asserted-by":"crossref","unstructured":"Heckerman D. Geiger D. &Chickering D. M.(1994).Learning Bayesian networks: The combination of knowledge and statistical data. InB. L.De Mantaras&D.Poole(Eds.) Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. 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