Analysis of straight-line data by Forman Sinnickson Acton

By Forman Sinnickson Acton

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If € is small, then 0; varies slowly and, loosely speaking, the "local" evolution of the g(O~, {~) can be treated as if the O~ were essentially constant. That is, for small € and tl. EOg(O,{n(O)). 4) 38 2. Applications where 9(0) = E9g(0'~n(0)) is a continuous function of O. This will be established in Chapter 8. 4) tries to exit the interval [a, b], then it is stopped on the boundary. Comment.

4) shows that 9(0) = 0 is equivalent to (under stationarity) the statement that there is equal probability that each route is full at the time of a call's arrival. 3) given later serves to equate the probabilities of being full in the long run. This might be called a "fairness to the user" criterion. Many other design goals can be realized with appropriate forms of the algorithm. 4 State Dependent Noise 37 pends on {O~} in a complicated way with significant memory. The dependence is of the Markovian type in that P {{~+1 = tl {i,Oi,i ~ n} = P {{~+1 = tl O~,{~}.

0 Outline of Chapter This chapter deals with more specific classes of examples, which are of increasing importance in current applications in many areas of technology. They are described in somewhat more detail than the examples of Chapter 1 are, and the illustration(s) given for each class are typical of those in a rapidly increasing literature. Section 1 deals with a problem in learning theory: the learning of an optimal hunting strategy by an animal, based on the history of successes and failures in repeated attempts to feed itself efficiently.

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