Data free inference with processed data products

Journal Article · Sat Jul 12 00:00:00 EDT 2014 · Statistics and Computing

DOI:https://doi.org/10.1007/s11222-014-9484-y· OSTI ID:1095511

Chowdhary, K. ^[1]; Najm, H. N. ^[1]

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1. Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

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.

• Research Organization: Sandia National Lab. (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1095511
• Report Number(s): SAND-2013-8500J; PII: 9484
• Journal Information: Statistics and Computing, Vol. 26, Issue 1-2; ISSN 0960-3174
• Publisher: Springer
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
uncertainty quantification
Bayesian inference
Markov Chain Monte Carlo
approximate Bayesian computation
maximum entropy
missing information