Mathematical Theory of Evidence in Navigation

Author(s):  
Włodzimierz Filipowicz
AIChE Journal ◽  
1987 ◽  
Vol 33 (11) ◽  
pp. 1930-1932 ◽  
Author(s):  
S. Narasimhan ◽  
Chen Shan Kao ◽  
R. S. H. Mah

Biometrics ◽  
1976 ◽  
Vol 32 (3) ◽  
pp. 703 ◽  
Author(s):  
A. F. M. Smith ◽  
Glenn Shafer

2016 ◽  
Vol 39 (1) ◽  
pp. 5-20 ◽  
Author(s):  
Marek Stawowy ◽  
Zbigniew Kasprzyk ◽  
Andrzej Szmigiel

Abstract The work describes the impact the reliability of the information quality IQ for information and communication systems. One of the components of IQ is the reliability properties such as relativity, accuracy, timeliness, completeness, consistency, adequacy, accessibility, credibility, congruence. Each of these components of IQ is independent and to properly estimate the value of IQ, use one of the methods of modeling uncertainty. In this article, we used a hybrid method that has been developed jointly by one of the authors. This method is based on the mathematical theory of evidence know as Dempstera-Shafera (DS) theory and serial links of dependent hybrid named IQ (hyb).


1988 ◽  
Vol 2 (4) ◽  
pp. 415-433 ◽  
Author(s):  
Jürg Kohlas

The mathematical theory of evidence (Shafer et al. [9]) has recently found much interest as an approach to treat uncertainty in expert and knowledge-based systems. Although the theory is very promising, there are not yet many practical applications. Modeling practice has still to be developed. This is a crucial task in view of facilitating the application of evidential modeling. It is the aim of this paper to discuss an important element of evidential modeling–conditional belief–within the scope of the mathematical theory of evidence.


2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Rattikorn Hewett

This paper presents an approach to automatically analyzing program spectra, an execution profile of program testing results for fault localization. Using a mathematical theory of evidence for uncertainty reasoning, the proposed approach estimates the likelihood of faulty locations based on evidence from program spectra. Our approach is theoretically grounded and can be computed online. Therefore, we can predict fault locations immediately after each test execution is completed. We evaluate the approach by comparing its performance with the top three performing fault localizers using a benchmark set of real-world programs. The results show that our approach is at least as effective as others with an average effectiveness (the reduction of the amount of code examined to locate a fault) of 85.6% over 119 versions of the programs. We also study the quantity and quality impacts of program spectra on our approach where the quality refers to the spectra support in identifying that a certain unit is faulty. The results show that the effectiveness of our approach slightly improves with a larger number of failed runs but not with a larger number of passed runs. Program spectra with support quality increases from 1% to 100% improves the approach's effectiveness by 3.29%.


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