Fabio Cuzzolin's Belief Functions: Theory and Applications: Third PDF

By Fabio Cuzzolin

ISBN-10: 3319111906

ISBN-13: 9783319111902

ISBN-10: 3319111914

ISBN-13: 9783319111919

This e-book constitutes the completely refereed complaints of the 3rd overseas convention on trust features, trust 2014, held in Oxford, united kingdom, in September 2014. The forty seven revised complete papers awarded during this publication have been conscientiously chosen and reviewed from fifty six submissions. The papers are equipped in topical sections on trust mix; desktop studying; functions; conception; networks; info fusion; info organization; and geometry.

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Read Online or Download Belief Functions: Theory and Applications: Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceedings PDF

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Additional info for Belief Functions: Theory and Applications: Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceedings

Sample text

In DS theory, m-functions are used to encode distinct bodies of evidence and combination rules are implemented to combine the m-functions. Suppose NS (NS ≥ 2) distinct bodies of evidence are associated with the NS number of mfunctions: m1 with focal elements A1i (1 ≤ i ≤ n1 ), m2 with focal elements A2j (1 ≤ j ≤ n2 ), and so on. ∩Ak S =∅ where C ⊆ X, C = ∅. (2) An Optimal Unified Combination Rule 41 (Dempster’s rule) Dempster’s rule of combination of multiple m-functions is defined as an orthogonal sum [18]: m∩ (C) , m∩ (∅) = 1, 1 − m∩ (∅) m1 ⊕ m2 ...

1. A set A ∈ 2X is called focal if m(A) > 0. 2. A belief function is called categorical if the body of evidence contains only one focal element B ∈ 2X . This belief function is denoted η B and obviously 1, B ⊆ A, . Using categorical belief functions, we can express η B (A) = 0, otherwise. any belief function by the formula Bel = m(B)η B . B∈2X 3. A belief measure is called a probability measure if m(A) = 0 for |A| > 1. ¯ bel and M ¯ pr the families of all belief func4. We denote correspondingly by M tions and probability measures on 2X , and if these families are normalized we denote them by Mbel and Mpr .

The chain of sources problem is an important and complex one, which has received different treatments in logic [4], possibility theory [1] and belief function theory [2,3]: in particular a solution involving successive corrections, precisely successive discountings, was proposed in [1]. The fact that contextual discounting may be relevant for this problem had not been remarked yet. 2 Contextual Reinforcement in Terms of BBCs t ,¬tp Let us consider another kind of contextual lie: the states hAx x , A ⊆ X , corresponding to the assumptions that the source is truthful for all x ∈ A and t ,¬tp a positive liar for all x ∈ A.

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Belief Functions: Theory and Applications: Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceedings by Fabio Cuzzolin


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