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This year, 2016, is the 40th anniversary of the publication of A Mathematical Theory of Evidence. Thierry Denoeux, the editor of International Journal of Approximate Reasoning, has asked me to use the occasion to reflect on how I came to write the book and how I experienced its subsequent reception. I am honored and pleased by this invitation. The occasion(More)
In this paper we give a simple account of local computation of marginal probabilities when the joint probability distribution is given in factored form and the sets of variables involved in the factors form a hypertree. Previous expositions of such local computation have emphasized conditional probability. We believe this emphasis is misplaced. What is(More)
Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability ǫ, together with a method that makes a prediction ŷ of a label y, it produces a set of labels, typically containing ŷ, that also contains y with probability 1− ǫ. Conformal prediction can be applied to any method for producing(More)
Probability and causality must live together because both involve contingency. When I assign a probability to a coin’s falling heads, I am saying that how it will fall is contingent. When I say the wind caused a tree to topple, I am saying that the wind and the toppling were contingent. The tree might have remained standing had the wind been less severe.(More)
От переводчика С разрешения авторов мы перевели первую главу их книги, и один из них (В. В.) смог найти время и просмотреть первые семь страниц перевода. Текст главы оказался весьма непривычным, а потому трудным для перевода, и мы совсем не уверены, что в нём не осталось ошибок. Надеемся, что В. В. сможет со временем просмотреть и исправить оставшуюся часть(More)
This article gives an algorithm for the exact implementation of Dempster’s rule in the case of hierarchical evidence. This algorithm is computationally efficient, and it makes the approximation suggested by Gordon and Shortliffe unnecessary. The algorithm itself is simple, but its derivation depends on a detailed understanding of the interaction of(More)
  • Glenn Shafer
  • Classic Works of the Dempster-Shafer Theory of…
  • 2008
The theory of belief functions assesses evidence by fitting it to a scale of canonical examples in which the meaning of a message depends on chance. In order to analyse parametric statistical problems within the framework of this theory, we must specify the evidence on which the parametric model is based. This article gives several examples to show how the(More)