scholarly journals A scoping inquiry into the potential contribution of Subjective Probability Theory, Dempster–Shafer Theory and Possibility Theory in accommodating degrees of belief in traveller behaviour research

2014 ◽  
Vol 1 (2) ◽  
pp. 45-56 ◽  
Author(s):  
David A. Hensher ◽  
Zheng Li
Author(s):  
Tazid Ali

Evidence is the essence of any decision making process. However in any situation the evidences that we come across are usually not complete. Absence of complete evidence results in uncertainty, and uncertainty leads to belief. The framework of Dempster-Shafer theory which is based on the notion of belief is overviewed in this chapter. Methods of combining different sources of evidences are surveyed. Relationship of probability theory and possibility theory to evidence theory is exhibited. Extension of the classical Dempster-Shafer Structure to fuzzy setting is discussed. Finally uncertainty measurement in the frame work of Dempster-Shafer structure is dealt with.


2013 ◽  
Vol 27 (2) ◽  
pp. 107-126 ◽  
Author(s):  
Rajendra P. Srivastava ◽  
Sunita S. Rao ◽  
Theodore J. Mock

ABSTRACT This study develops a framework for planning, performing, and evaluating evidence obtained to assess and control the risks of providing assurance on sustainability reports. Sustainability reporting, or corporate sustainability reporting (CSR), provides stakeholders with important information on both financial and non-financial factors related to environmental, social, and economic performance. Importantly, the presented framework is developed from both a Bayesian (probability-based theory) and Belief Function (Dempster-Shafer theory) perspective. This facilitates application of the framework to cases where the assurance provider prefers to assess risk in terms of probability versus in terms of beliefs. To demonstrate the application of this framework we evaluate assertions, sub-assertions, and audit evidence relevant to CSR based on the G3 Reporting framework developed by the Global Reporting Initiative (GRI). The paper contributes to the literature in three main areas. First, it demonstrates how evidence-based reasoning can be used for engagements where different levels of assurance are provided for the assertions being audited. Second, it shows how various items of evidence at different levels may be aggregated. Third, it presents a generic theoretical model for assuring information based on belief-based assessments, which is then contrasted with a theoretical model based on probability theory. In contrasting the two approaches, we show that in cases where initial uncertainty is substantial, the use of Dempster-Shafer theory has advantages over probability theory in terms of efficiency in achieving a targeted low level of assurance.


2015 ◽  
pp. 105-124
Author(s):  
Tazid Ali

Evidence is the essence of any decision making process. However in any situation the evidences that we come across are usually not complete. Absence of complete evidence results in uncertainty, and uncertainty leads to belief. The framework of Dempster-Shafer theory which is based on the notion of belief is overviewed in this chapter. Methods of combining different sources of evidences are surveyed. Relationship of probability theory and possibility theory to evidence theory is exhibited. Extension of the classical Dempster-Shafer Structure to fuzzy setting is discussed. Finally uncertainty measurement in the frame work of Dempster-Shafer structure is dealt with.


Author(s):  
Hans W. Guesgen ◽  
Stephen Marsland

The recognition of human behaviour from sensor observations is an important area of research in smart homes and ambient intelligence. In this chapter, the authors introduce the idea of spatio-temporal footprints, which are local patterns in space and time that should be similar across repeated occurrences of the same behaviour. They discuss the spatial and temporal mapping requirements of these footprints, together with how they may be used. As possible formalisms for implementing spatio-temporal footprints, the authors discuss and evaluate probability theory, fuzzy sets, and the Dempster-Shafer theory.


2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.


Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.


Energy ◽  
1992 ◽  
Vol 17 (3) ◽  
pp. 205-214
Author(s):  
Aurora A. Kawahara ◽  
Peter M. Williams

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