Titlebook: Stochastic Processes - Inference Theory; Malempati M. Rao Book 2014Latest edition Springer International Publishing Switzerland 2014 60Gxx

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2否定

Principles of Hypothesis Testing,e fundamental lemma of Neyman-Pearson, in its abstract version due to Grenander, and a few of its applications as well as a technique in reducing composite hypotheses by means of weights. The latter contains a detailed Bayes methodology with iterated priors and some uniformity conditions that admit extensions to stochastic processes.

左右连贯

Sampling and Regression for Processes,, if they present the essential characteristics of the process on the bigger segment. A basic result in this direction for second order processes is the one due independently to Kotel’nikov and Shannon, and we present some results of this type for the stationary as well as some general processes, in Section 1.

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More on Stochastic Inference,r is composite, several new problems arise. In this chapter, we consider some of these questions in detail. The results again depend on likelihood ratios, and an extension of the Neyman-Pearson-Grenander theorem is once more of importance.

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978-3-319-37434-5Springer International Publishing Switzerland 2014

anatomical

Stochastic Processes - Inference Theory978-3-319-12172-7Series ISSN 1439-7382 Series E-ISSN 2196-9922

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META

Gustatory

NATTY

Prediction and Filtering of Processes,This chapter is devoted to a different class of applications complementing the preceding work. The first section contains a comparative analysis of general prediction operations relative to a convex loss function, and its relation to projection operators.