Titlebook: Probability via Expectation; Peter Whittle Textbook 2000Latest edition Springer Science+Business Media New York 2000 Markov process.Marti

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Stochastic Convergence, of r.v.s. 282-1 which one suspects has some kind of limit property for large . is a familiar object. For example, the convergence of the sample average . to a common expected value .(.) (in mean square, Exercise 2.8.6; in probability, Exercise 2.9.14 or in distribution, Section 7.3) has been a recu

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Large-Deviation Theory, the beauty of the theory and of the many ways in which it can be viewed, but even more because of the realisation that it provides the natural tool in so many applications. Roughly, it applies to systems that have a large-scale character (in a sense to be defined) and exploits this scale to derive

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Extension: Examples of the Infinite-Dimensional Case,nitely many expectation values are specified. The two issues to be faced remain those of consistency and extension. These issues are too large for us to treat systematically, and we shall in fact consider only some particular cases of extension, of interest either in that they make a point or in tha

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Peter Whittlehould not only be based on its advantages but also on its shortcomings, and clinicians should be aware of the risks of untoward or unexpected events. Malfunction of the da Vinci robotic system is one of the shortcomings that might result in variable outcomes, depending on the severity. The potential