Study Of The Different Methods For Combining Evidence

Yizong Cheng; Rangasami L. Kashyap

doi:10.1117/12.964153

26 March 1986 Study Of The Different Methods For Combining Evidence

Yizong Cheng, Rangasami L. Kashyap

Proceedings Volume 0635, Applications of Artificial Intelligence III; (1986) https://doi.org/10.1117/12.964153
Event: 1986 Technical Symposium Southeast, 1986, Orlando, United States

Abstract

Suppose there are several mutually exclusive hypotheses H₁, ..., H_n about the state of nature. Let B(H_i|E) denote the conditional belief that H_i is true given the evidence E. The conditional belief can be a real number in the range [0, 1], an interval of these numbers, or a linguistic valuable like "likely". Let B(H_i|E) and B(H_i|E₂) stand for the truth of the hypothesis H_i given evidence E₁ and E₂ separately, E₁ and E₂ being two "independent" bodies of evidence (the concept of independence will be discussed later). Let B(H_i|E₁E₂) denote the belief in the truth of H_i given both E₁ and E₂. E₁ and E₂ together may also be considered as a body of evidence, but we distinguish it from E₁ or E₂ alone by calling the former compound and the latter atomic. Clearly it is some function of the individual belief variables B(H_i|E) and B(H_i|E₂), i=1,...,n. There are several methods for computing the integrated belief or conditional probability function from its constituents, two notable ones being the Bayesian approach and the Dempster-Shafer approach with its historical antecedents due to Bernoulli and Lambert. Our aim in this paper is to study the axioms and consequences of the various approaches and evaluate whether they satisfy some common axioms which decision makers expect from these rules for combining various types of evidence.

Citation Download Citation

Yizong Cheng and Rangasami L. Kashyap "Study Of The Different Methods For Combining Evidence", Proc. SPIE 0635, Applications of Artificial Intelligence III, (26 March 1986); https://doi.org/10.1117/12.964153

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