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Uncertainty-Sensitive Heterogeneous Information Fusion: Assessing Threat with Soft, Uncertain, and Conflicting Evidence [Pehme köide]

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  • Formaat: Paperback / softback, 168 pages, kõrgus x laius x paksus: 230x150x10 mm, kaal: 276 g
  • Ilmumisaeg: 29-Feb-2016
  • Kirjastus: RAND
  • ISBN-10: 0833092774
  • ISBN-13: 9780833092779
Teised raamatud teemal:
Uncertainty-Sensitive Heterogeneous Information Fusion: Assessing Threat with Soft, Uncertain, and Conflicting EvidenceSuurem pilt
  • Formaat: Paperback / softback, 168 pages, kõrgus x laius x paksus: 230x150x10 mm, kaal: 276 g
  • Ilmumisaeg: 29-Feb-2016
  • Kirjastus: RAND
  • ISBN-10: 0833092774
  • ISBN-13: 9780833092779
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780833092779.html
Märksõnad:
Presents research on methods for heterogeneous information fusion—combining data that are qualitative, subjective, fuzzy, ambiguous, contradictory, and even deceptive, in order to form a realistic assessment of threat in a counterterrorism context.
Preface iii
Summary xi
Acknowledgments xxv
Chapter One Introduction
1(10)
Objective
2(1)
Research Strategy and an Idealized Fusion Process
3(1)
Analytic Architecture for Uncertainty-Sensitive Fusion
4(6)
About This Report
10(1)
Chapter Two Concepts, Methods, and a Research Platform
11(12)
Research Platform
11(1)
Representing Qualitative and Quantitative Information Consistently
12(2)
Expressing Simple Uncertainty
14(1)
Expressing More Complex Forms of Uncertainty
15(6)
Subjective Estimates of Quality, Credibility, Salience, and Reliability
21(2)
Chapter Three Creating Synthetic Data: Vignettes for Testing
23(12)
Approach
23(2)
Harry Smith and the Slammers
25(4)
Ahmed al-Hiry and the al-Hasqua Group
29(6)
Chapter Four Simple and Bayesian Fusion Methods
35(30)
Direct Subjective Methods
36(1)
Subjective Algebraic Methods
37(7)
Quasi-Bayesian Fusion
44(14)
Reflecting Equivocal Evidence
58(2)
Quality and Reliability: Quasi-Bayesian Analysis
60(5)
Chapter Five The Maximum Entropy/Minimum Penalty Fusion Method
65(18)
Overview
65(4)
Being Conservative: Maximizing Uncertainty When Assessing What We Really Know
69(2)
Modeling What Has Been Reported About a Subject
71(8)
Specification for an Illustrative Application Using the PFT Model
79(1)
Computational Considerations
80(1)
Next Steps for MEMP
81(2)
Chapter Six Illustrative Analysis
83(20)
A Flexible Suite of Fusion Methods
83(7)
Other Explorations
90(6)
Dealing with Bias and Related Correlations
96(5)
Summary Observations from Experiments
101(2)
Chapter Seven Conclusions and Recommendations
103(4)
APPENDIXES
A Defining Threat
107(10)
B A Factor-Tree Model for Propensity for Terrorism (PFT)
117(6)
C Extending Elementary Bayes Updating
123(6)
Abbreviations 129(2)
Glossary 131(4)
Bibliography 135