&lt;title&gt;Comparison of Bayesian and Dempster-Shafer theory for sensing: a practitioner's approach&lt;/title&gt;

Hoffman, James C.; Murphy, Robin R.

doi:10.1117/12.162045

Cited by 14 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, several works [15,23] have pointed out that while Bayes' theorem is most suited to problems where there are probabilities for all events, it is least suited to problems where there is partial or complete ignorance, and limited or conflicting information. The Dempster-Shafer theory, on the other hand, can model various types of partial ignorance and limited or conflicting evidence: it is a more flexible model than Bayes' theorem.…”

Section: Probabilistic Lexical Annotationmentioning

confidence: 97%

Automatic generation of probabilistic relationships for improving schema matching

Sorrentino

2011

Information Systems

View full text Add to dashboard Cite

Section: Probabilistic Lexical Annotationmentioning

confidence: 97%

Automatic generation of probabilistic relationships for improving schema matching

Sorrentino

2011

Information Systems

View full text Add to dashboard Cite

“…[8,9] also discussed the problems of target classification within the TBM framework. Comparisons between the Bayesian classifier and the classifier based on the Dempster-Shafer theory lead to the general conclusion that the latter is more robust than the former [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 97%

Recursive TBM method for target classification

Shan¹,

Mei²,

Cheng³

2009

SPIE Proceedings

View full text Add to dashboard Cite

Target classification based on the transferable belief model (TBM) is believed to be more robust than the Bayesian method. However, existing TBM classifier may forget over time the estimated prior information of the class. This paper proposes a recursive TBM classifier, which could combine the current basic belief assignment (BBA) of the class with the historic class information. Besides, feature mapping from the feature space to the class space, instead of the conventional converse mapping, is utilized to improve the performance of the recursive classifier. Simulation results reveal that the proposed TBM classifier eliminated the deficiency of existing TBM method and has more robust performance than the Bayesian classifier.

show abstract

“…A comparison of the Bayesian and DS approaches in [25] illustrates that the two techniques can lead to similar results but that a well-defined formulation of probabilities, conditioning and priors is required in the Bayes method, whereas in the DS theory, conditioning is incorporated in the belief functions with no prior knowledge needed. A discussion of the similarities and differences between the two approaches is also given in [26].…”

Section: Dempster-shafer Theorymentioning

confidence: 99%

A belief-based approach to spectrum occupancy mapping

Willink

2013

J Wireless Com Network

View full text Add to dashboard Cite

Maintaining a map of signal power levels is one approach to dynamic spectrum management, but generating it in a complex environment with unknown emitters using sensor measurements is challenging. Precise representation of signal levels using sparse sensors is not feasible in realistic conditions where measurements may be inaccurate and the propagation conditions are uncertain. The goal is to use sensor power measurements to identify regions where the signal level exceeds, or falls below, a given threshold, and to provide a level of confidence in those determinations. In this work, a belief-based method using Dempster-Shafer analysis is developed, which can accommodate uncertainties due to the propagation conditions and sensor inaccuracies and combines evidence from different sensors to give a belief value for each state. The method is illustrated first for a simplistic, flat-earth model, then the impacts of parameter uncertainties, shadowing and sensor errors are incorporated. A real operating environment is emulated using a propagation prediction program, and it is demonstrated that the new approach is able to provide useful input to the spectrum management function and enables a sophisticated interpretation to support context-specific decision-making.

show abstract

<title>Comparison of Bayesian and Dempster-Shafer theory for sensing: a practitioner's approach</title>

Cited by 14 publications

References 7 publications

Automatic generation of probabilistic relationships for improving schema matching

Automatic generation of probabilistic relationships for improving schema matching

Recursive TBM method for target classification

A belief-based approach to spectrum occupancy mapping

Contact Info

Product

Resources

About