Inference Given Summary Statistics

Habib N. Najm () and Kenny Chowdhary ()
Additional contact information
Habib N. Najm: Sandia National Laboratories, Combustion Research Facility, Reacting Flow Research
Kenny Chowdhary: Sandia National Laboratories, Quantitative Modeling and Analysis

Chapter 3 in Handbook of Uncertainty Quantification, 2017, pp 33-67 from Springer

Abstract: Abstract In many practical situations, where one is interested in employing Bayesian inference methods to infer parameters of interest, a significant challenge is that actual data is not available. Rather, what is most commonly available in the literature are summary statistics on the data, on parameters of interest, or on functions thereof. In this chapter, we present a general framework relying on the maximum entropy principle, and employing approximate Bayesian computation methods, to infer a joint posterior density on parameters of interest given summary statistics, as well as other known details about the experiment or observational system behind the published statistics. By essentially redoing the experimental fitting using proposed data sets, the method ensures that the inferred joint posterior density on model parameters is consistent with the given statistics and with the model.

Keywords: Approximate bayesian computation; Bayesian inference; Maximum entropy; Missing data; Sufficient statistic (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12385-1_68

Ordering information: This item can be ordered from
http://www.springer.com/9783319123851

DOI: 10.1007/978-3-319-12385-1_68

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().