By Russell G. Almond
This cutting edge quantity explores graphical types utilizing trust capabilities as a illustration of uncertainty, delivering an alternate method of difficulties the place chance proves insufficient. Graphical trust Modeling makes it effortless to check the 2 methods whereas comparing their relative strengths and limitations.
The writer examines either concept and computation, incorporating sensible notes from the author's personal adventure with the idea software program package deal. As one of many first volumes to use the Dempster-Shafer trust services to a pragmatic version, a considerable component to the ebook is dedicated to a unmarried example--calculating the reliability of a posh method. This distinctive characteristic allows readers to achieve a radical realizing of the appliance of this methodology.
The first part offers an outline of graphical trust types and probablistic graphical types that shape a big subset: the second one part discusses the set of rules utilized in the manipulation of graphical versions: the ultimate section of the e-book bargains an entire description of the danger overview instance, in addition to the technique used to explain it.
Graphical trust Modeling bargains researchers and graduate scholars in man made intelligence and information greater than only a new method of an outdated reliability activity: it offers them with a useful representation of the method of graphical trust modeling.
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Extra info for Graphical Belief Modeling
Those few ideas discussed in this book that are not part of BELIEF are either currently available in GRAPHICAL-BELIEF or are planned for future enhancement. 6 BRIEF DESCRIPTION OF CONTENTS 21 The LOCA fault tree from the IREP study (Part III) has served as an extensive test case for BELIEF. It required several important extensions to BELIEF, particularly the ability to store second-order models and perform the Monte Carlo algorithm described in Chapters 11 and 12. All of the examples worked in this book were explored with the BELIEF package.
1 Basic Definitions Probability is a measure associated with an experiment whose outcome is unknown. • } of possible outcomes. This is the outcome space, or following the belief function terminology introduced in the next chapter, the frame of discernment or frame (although strictly speaking the frame is the outcome space which is the focus of our current attention, implying that our focus can be wider or narrower). " The set A ~ e is known as an event. 1. Balls in an Urn. Consider an urn which contains w white balls and b black balls.
Another dass of models that has been proposed for uncertain phenomena problems is fuzzy sets. Fuzzy sets, however, are models for imprecision (specifically the imprecision of naturallanguage) rather than uncertainty. Because most of our models for the generation of data involve uncertainty (probability), fuzzy models are more difficult to update than probability or belief function models. 2 In risk assessment, we would like to have precise models of uncertain events. This makes probabilities the preferred model, with belief functions an alternative in the case of imprecise information.