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Title: Data free inference with processed data products

Here, we consider the context of probabilistic inference of model parameters given error bars or confidence intervals on model output values, when the data is unavailable. We introduce a class of algorithms in a Bayesian framework, relying on maximum entropy arguments and approximate Bayesian computation methods, to generate consistent data with the given summary statistics. Once we obtain consistent data sets, we pool the respective posteriors, to arrive at a single, averaged density on the parameters. This approach allows us to perform accurate forward uncertainty propagation consistent with the reported statistics.
Authors:
 [1] ;  [1]
  1. Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Publication Date:
OSTI Identifier:
1095511
Report Number(s):
SAND--2013-8500J
Journal ID: ISSN 0960-3174; PII: 9484
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Statistics and Computing
Additional Journal Information:
Journal Volume: 26; Journal Issue: 1-2; Journal ID: ISSN 0960-3174
Publisher:
Springer
Research Org:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING uncertainty quantification; Bayesian inference; Markov Chain Monte Carlo; approximate Bayesian computation; maximum entropy; missing information