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Title: Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.


Abstract not provided.

; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Third Santa Fe Conference on Global and Regional Climate Change held October 31 - November 4, 2011 in Santa Fe, NM.
Country of Publication:
United States

Citation Formats

Field, Richard V.,, Boslough, Mark Bruce Elrick, and Constantine, Paul G. Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.. United States: N. p., 2011. Web.
Field, Richard V.,, Boslough, Mark Bruce Elrick, & Constantine, Paul G. Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.. United States.
Field, Richard V.,, Boslough, Mark Bruce Elrick, and Constantine, Paul G. Sat . "Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.". United States. doi:.
title = {Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.},
author = {Field, Richard V., and Boslough, Mark Bruce Elrick and Constantine, Paul G},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Oct 01 00:00:00 EDT 2011},
month = {Sat Oct 01 00:00:00 EDT 2011}

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  • PRAs often require quantification of the probabilities of various low-probability events, such as accident-initiating events and hardware-fault events. A two-stage Bayes/empirical Bayes data pooling procedure is presented for use in combining as many as five different types of relevant data. A Poisson model is assumed for the event in question. Empirical Bayes methods are used to determine the population variability curve, while Bayesian methods are used to specialize this curve to the specific event in question. The procedure is illustrated by an example in which we estimate the probability of failure of a hypothetical large dam based on (1) amore » deductive event-tree-type analysis of the probability, (2) historical US dam failure data, (3) the opinions of a committee of several dam experts, and (4) the operating history for the dam in question. A Stage-2 posterior distribution is produced which incorporates these data sources. Similar distributions are produced for various combinations of data types and used to assess the contribution of each data source.« less