Prediction and the quantification of uncertainty

J. Glimm; D. H. Sharp

doi:10.1016/S0167-2789(99)00103-7

Prediction and the quantification of uncertainty

J. Glimm
, D. H. Sharp

Applied Mathematics and Statistics

Los Alamos National Laboratory Theoretical Division

Research output: Contribution to journal › Article › peer-review

50 Scopus citations

Abstract

Prediction is based on the comparison of results from the statistical analysis of observational data and from the scientific modeling of the system being observed. Effective prediction imposes new as well as familiar requirements on observation and scientific modeling, as will be reviewed here. We emphasize issues specific to prediction in the context of technology. Recent results of the authors, colleagues, and others which address these requirements will be presented.

Original language	English
Pages (from-to)	152-170
Number of pages	19
Journal	Physica D: Nonlinear Phenomena
Volume	133
Issue number	1-4
DOIs	https://doi.org/10.1016/S0167-2789(99)00103-7
State	Published - Sep 10 1999

Keywords

02.50.-r
02.50.Wp
02.70.Lq
47.55.Kf
47.55.Mh
Bayesian inference
Markov Chain Monte Carlo
Multiphase flow

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10.1016/S0167-2789(99)00103-7

Cite this

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KW - 02.70.Lq

KW - 47.55.Kf

KW - 47.55.Mh

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KW - Markov Chain Monte Carlo

KW - Multiphase flow

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Prediction and the quantification of uncertainty

Abstract

Keywords

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