New PDF release: A Mathematical Theory of Arguments for Statistical Evidence

By Paul-Andre Monney

ISBN-10: 3642517463

ISBN-13: 9783642517464

ISBN-10: 3790815276

ISBN-13: 9783790815276

The topic of this e-book is the reasoning less than uncertainty in accordance with sta­ tistical proof, the place the note reasoning is taken to intend trying to find arguments in want or opposed to specific hypotheses of curiosity. the type of reasoning we're utilizing consists of 2 features. the 1st one is galvanized from classical reasoning in formal good judgment, the place deductions are made of an information base of saw proof and formulation representing the area spe­ cific wisdom. during this booklet, the proof are the statistical observations and the final wisdom is represented by means of an example of a unique form of sta­ tistical types known as useful versions. the second one point offers with the uncertainty lower than which the formal reasoning occurs. For this element, the speculation of tricks [27] is the proper device. primarily, we think that a few doubtful perturbation takes a particular worth after which logically eval­ uate the results of this assumption. the unique uncertainty in regards to the perturbation is then transferred to the implications of the belief. this type of reasoning is named assumption-based reasoning. ahead of going into extra information about the content material of this ebook, it'd be attention-grabbing to seem in brief on the roots and origins of assumption-based reasoning within the statistical context. In 1930, R. A. Fisher [17] outlined the suggestion of fiducial distribution because the results of a brand new type of argument, in place of the results of the older Bayesian argument.

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All its focal sets are nested [27]. 7 of Kohlas & Monney [27], we can easily show that Wm n(H, H') , = max {lm,n(B) : () E H} . max {lm,n(()) : () E H'} The situation where, in addition to x and x', other values are observed can be treated in a similar way. 3 Functional and Distribution Models We have seen that a generalized functional model allows us to compute the weight of evidence for composite hypotheses. The problem is that in general there exist several GFM having the same associated distribution model.

8 Prior Information In a situation where the initial knowledge about the unknown parameter contains a prior probability distribution Po on 8, it is easy to include this information into a generalized functional model (J, Pl. Indeed, such a prior information can be viewed as a precise hint on 8 given by 1-io = (8, Po, r, 8) where T( 8) = {8} for all 8 E e. The support and plausibility functions of this hint is nothing but the prior probability distribution Po. Then this hint 1-io is combined with the hint 1-i(XI, ...

Since pl(W) is the probability of the outcome w, it follows that w in v x(8) supports the hypothesis that e* is in Fx(w) to the degree PI(W). In other words, w in v x (8) is an argument in favor of the hypothesis Fx(w) and pl(W) represents the weight of this argument. Finally, note that Fx(w) is not empty for all w E v x (8). 6 Generalized Functional Models and Hints The goal of this section is to show how the knowledge about e generated by the observations in a generalized functional model can be expressed by means of the theory of hints, a theory strongly related to the Dempster-Shafer theory of evidence [27,38].

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A Mathematical Theory of Arguments for Statistical Evidence by Paul-Andre Monney


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