Disclose
发表于 2025-3-21 18:53:27
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字的误用
发表于 2025-3-21 23:29:12
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vibrant
发表于 2025-3-22 00:51:16
Gaussian and Non-Gaussian Linear Time Series and Random Fields
instructive
发表于 2025-3-22 04:36:35
Book 2000e increasing in the usual manner is the same as that with time reversed. Chapter 1 considers the question of reversibility for linear stationary sequences and gives necessary and sufficient conditions for the reversibility. A neat result of Breidt and Davis on reversibility is presented. A sim ple
改良
发表于 2025-3-22 09:22:06
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改正
发表于 2025-3-22 12:57:31
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改正
发表于 2025-3-22 19:20:03
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TOM
发表于 2025-3-23 00:20:39
Bedingte Reflexe, die Lernmatrix,Later on a number of methods will be introduced that are based on moments of cumulants and are used to estimate aspects of the structure of processes of interest. For this reason it seems proper to make some remarks about moments and cumulants and the relationship between them.
granite
发表于 2025-3-23 04:00:52
https://doi.org/10.1007/978-3-662-00604-7Assume that . is a stationary ARMA scheine satisfying the system of equations. where the ξ.’s are independent and identically distributed with .ξ. = 0 and .ξ. = σ. > 0. Consider the prediction problem in which one approximates x. by a function of x., s ≤ 0, in mean square as well as one can.
翻布寻找
发表于 2025-3-23 07:53:07
Reversibility and Identifiability,Let us first consider linear stationary sequences. A sequence of independent, identically distributed real random variables ξ., j = …, -1,0,1,… is given with Eξ. = 0, 0 < .ξ. = σ. < ∞. The process x. is obtained by passing this sequence through a linear filter characterized by the real weights, ., ∑. < ∞,