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Computing Statistics under Interval and Fuzzy Uncertainty
Computing Mean under Interval Uncertainty
other
Author(s):
Hung T. Nguyen
,
Vladik Kreinovich
,
Berlin Wu
,
Gang Xiang
Publication date
(Print):
2012
Publisher:
Springer Berlin Heidelberg
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Electronic Workshops in Computing (eWiC)
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Book Chapter
Publication date (Print):
2012
Page
: 63
DOI:
10.1007/978-3-642-24905-1_12
SO-VID:
663a36a9-2e4e-4212-88a6-d2b664e67c6e
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Book chapters
pp. 3
Formulation of the Problem
pp. 9
Computing Statistics under Probabilistic and Interval Uncertainty: A Brief Description
pp. 11
Computing Statistics under Fuzzy Uncertainty: Formulation of the Problem
pp. 19
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty
pp. 25
Computing under Interval Uncertainty: Traditional Approach Based on Uniform Distributions
pp. 29
Computing under Interval Uncertainty: When Measurement Errors Are Small
pp. 35
Computing under Interval Uncertainty: General Algorithms
pp. 47
Computing under Interval Uncertainty: Computational Complexity
pp. 51
Towards Selecting Appropriate Statistical Characteristics: The Basics of Decision Theory and the Notion of Utility
pp. 55
How to Select Appropriate Statistical Characteristics
pp. 61
Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty: Reminder
pp. 63
Computing Mean under Interval Uncertainty
pp. 65
Computing Median (and Quantiles) under Interval Uncertainty
pp. 67
Computing Variance under Interval Uncertainty: An Example of an NP-Hard Problem
pp. 79
Types of Interval Data Sets: Towards Feasible Algorithms
pp. 95
Computing Variance under Interval Uncertainty: Efficient Algorithms
pp. 119
Computing Variance under Hierarchical Privacy-Related Interval Uncertainty
pp. 129
Computing Outlier Thresholds under Interval Uncertainty
pp. 153
Computing Higher Moments under Interval Uncertainty
pp. 167
Computing Mean, Variance, Higher Moments, and Their Linear Combinations under Interval Uncertainty: A Brief Summary
pp. 169
Computing Covariance under Interval Uncertainty
pp. 173
Computing Correlation under Interval Uncertainty
pp. 177
Computing Expected Value under Interval Uncertainty
pp. 181
Computing Entropy under Interval Uncertainty. I
pp. 193
Computing Entropy under Interval Uncertainty. II
pp. 211
Computing the Range of Convex Symmetric Functions under Interval Uncertainty
pp. 221
Computing Statistics under Interval Uncertainty: Possibility of Parallelization
pp. 225
Computing Statistics under Interval Uncertainty: Case of Relative Accuracy
pp. 237
How Reliable Is the Input Data?
pp. 243
How Accurate Is the Input Data?
pp. 251
From Computing Statistics under Interval and Fuzzy Uncertainty to Practical Applications: Need to Propagate the Statistics through Data Processing
pp. 261
Applications to Bioinformatics
pp. 265
Applications to Computer Science: Optimal Scheduling for Global Computing
pp. 277
Applications to Information Management: How to Estimate Degree of Trust
pp. 283
Applications to Information Management: How to Measure Loss of Privacy
pp. 289
Application to Signal Processing: Using 1-D Radar Observations to Detect a Space Explosion Core among the Explosion Fragments
pp. 299
Applications to Computer Engineering: Timing Analysis of Computer Chips
pp. 305
Applications to Mechanical Engineering: Failure Analysis under Interval and Fuzzy Uncertainty
pp. 317
Applications to Geophysics: Inverse Problem
pp. 333
Need to Go Beyond Interval and Fuzzy Uncertainty
pp. 335
Beyond Interval Uncertainty: Taking Constraints into Account
pp. 349
Beyond Interval Uncertainty: Case of Discontinuous Processes (Phase Transitions)
pp. 357
Beyond Interval Uncertainty in Describing Statistical Characteristics: Case of Smooth Distributions and Info-Gap Decision Theory
pp. 367
Beyond Traditional Interval Uncertainty in Describing Statistical Characteristics: Case of Interval Bounds on the Probability Density Function
pp. 379
Beyond Interval Uncertainty in Describing Statistical Characteristics: Case of Normal Distributions
pp. 391
Beyond Traditional Fuzzy Uncertainty: Interval-Valued Fuzzy Techniques
pp. 395
Beyond Traditional Fuzzy Uncertainty: Type-2 Fuzzy Techniques
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