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Title: Data-Driven Uncertainty Quantification for Multi-Sensor Analytics.

Abstract

Abstract not provided.

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1507484
Report Number(s):
SAND2018-3541C
662249
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SPIE Defense + Security held April 16-18, 2018 in Orlando, FL.
Country of Publication:
United States
Language:
English

Citation Formats

Stracuzzi, David John, Darling, Michael Christopher, Chen, Maximillian Gene, and Peterson, Matthew Gregor. Data-Driven Uncertainty Quantification for Multi-Sensor Analytics.. United States: N. p., 2018. Web.
Stracuzzi, David John, Darling, Michael Christopher, Chen, Maximillian Gene, & Peterson, Matthew Gregor. Data-Driven Uncertainty Quantification for Multi-Sensor Analytics.. United States.
Stracuzzi, David John, Darling, Michael Christopher, Chen, Maximillian Gene, and Peterson, Matthew Gregor. Sun . "Data-Driven Uncertainty Quantification for Multi-Sensor Analytics.". United States. https://www.osti.gov/servlets/purl/1507484.
@article{osti_1507484,
title = {Data-Driven Uncertainty Quantification for Multi-Sensor Analytics.},
author = {Stracuzzi, David John and Darling, Michael Christopher and Chen, Maximillian Gene and Peterson, Matthew Gregor},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {4}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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