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Titlebook: Introduction to Random Processes; Yuriĭ A. Rozanov Book 1987 Springer-Verlag Berlin Heidelberg 1987 Brownian motion.Markov process.Random

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书目名称Introduction to Random Processes
编辑Yuriĭ A. Rozanov
视频video
丛书名称Springer Series in Soviet Mathematics
图书封面Titlebook: Introduction to Random Processes;  Yuriĭ A. Rozanov Book 1987 Springer-Verlag Berlin Heidelberg 1987 Brownian motion.Markov process.Random
描述Today, the theory of random processes represents a large field of mathematics with many different branches, and the task of choosing topics for a brief introduction to this theory is far from being simple. This introduction to the theory of random processes uses mathematical models that are simple, but have some importance for applications. We consider different processes, whose development in time depends on some random factors. The fundamental problem can be briefly circumscribed in the following way: given some relatively simple characteristics of a process, compute the probability of another event which may be very complicated; or estimate a random variable which is related to the behaviour of the process. The models that we consider are chosen in such a way that it is possible to discuss the different methods of the theory of random processes by referring to these models. The book starts with a treatment of homogeneous Markov processes with a countable number of states. The main topic is the ergodic theorem, the method of Kolmogorov‘s differential equations (Secs. 1-4) and the Brownian motion process, the connecting link being the transition from Kolmogorov‘s differential-diff
出版日期Book 1987
关键词Brownian motion; Markov process; Random variable; diffusion process; ergodic theory; filtration; random wa
版次1
doihttps://doi.org/10.1007/978-3-642-72717-7
isbn_softcover978-3-642-72719-1
isbn_ebook978-3-642-72717-7Series ISSN 0939-1169
issn_series 0939-1169
copyrightSpringer-Verlag Berlin Heidelberg 1987
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Stochastic Differential Equations,In this paragraph we shall consider (real) random processes ξ(.), . ≥., characterized by the stochastic differential . where .), .) are non-random functions of the parameters . ≥ . and —∞ < . < ∞ .
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Diffusion Processes,Suppose that the probability densities .(., ., ., .), -∞ < . < ∞, depend on the parameters . > . > ., -∞ < . < ∞ in such a way, that the . holds: ..
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https://doi.org/10.1007/978-3-642-72717-7Brownian motion; Markov process; Random variable; diffusion process; ergodic theory; filtration; random wa
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