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Applications to Learning, State Dependent Noise, and Queueing,y 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合同 发表于 2025-3-22 16:12:29
Applications in Signal Processing and Adaptive Control,d there is a large amount of literature concerning them. Only a few references will be listed. Despite the identity in form, whether the algorithms are actually called . depends on the traditions of the field, but the most effective techniques of proofs are often based on those of stochastic approxiMechanics 发表于 2025-3-22 20:49:38
Mathematical Background,ne of the basic processes in stochastic analysis. It appears frequently as a “noise” term in the decomposition of the stochastic approximation algorithm, which will be used to facilitate the analysis. This was already apparent in many examples in Chapters 1 and 2, where the noise had the martingale轻推 发表于 2025-3-22 22:26:16
Convergence with Probability One: Martingale Difference Noise,hat is, where there is a function ..(·) of θ such that . [..., . < ., θ.] = ..(θ.) . Then we can write .. = ..(θ.) + δ.. where δ.. is a martingale difference. This “martingale difference noise” model is still of considerable importance. It arises,FAST 发表于 2025-3-23 01:55:51
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