Using Influence Diagrams to Solve a Calibration Problem

R. E. Barlow, R. W. Mensing and N. G. Smiriga
Additional contact information
R. E. Barlow: University of California
R. W. Mensing: Lawrence Livermore National Laboratory
N. G. Smiriga: Lawrence Livermore National Laboratory

A chapter in Probability and Bayesian Statistics, 1987, pp 17-30 from Springer

Abstract: Abstract A measuring instrument measures a unit and records an observation y. The non-measurable variable of interest, the “true” measurement, x, of the unit is to be inferred from y, the measurable variable. If P(y|x) is the likelihood of y given x and x has prior p(x), then by Bayes’ Theorem $$ {\rm{p}}({\rm{x}}|{\rm{y}}) \propto {\rm{p}}({\rm{y}}|{\rm{x}}){\rm{p(x)}}{\rm{.}} $$

Date: 1987
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:978-1-4613-1885-9_3

Ordering information: This item can be ordered from
http://www.springer.com/9781461318859

DOI: 10.1007/978-1-4613-1885-9_3

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 ().