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Titlebook: Stationary Processes and Discrete Parameter Markov Processes; Rabi Bhattacharya,Edward C. Waymire Textbook 2022 Springer Nature Switzerlan

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21#
發(fā)表于 2025-3-25 06:56:50 | 只看該作者
https://doi.org/10.1007/978-3-031-00943-3weakly stationary processes; discrete parameter Markov processes; spectral representation of a station
22#
發(fā)表于 2025-3-25 07:51:26 | 只看該作者
Springer Nature Switzerland AG 2022
23#
發(fā)表于 2025-3-25 11:44:18 | 只看該作者
24#
發(fā)表于 2025-3-25 17:37:08 | 只看該作者
25#
發(fā)表于 2025-3-25 20:57:51 | 只看該作者
Martingale Central Limit Theorem, moments that encompass a wide range of applications that extend well beyond the classical formulations for i.i.d. summands. The approach is based upon infinitesimal conditions for a stochastic process to be a Gaussian process of interest in their own right.
26#
發(fā)表于 2025-3-26 03:32:26 | 只看該作者
27#
發(fā)表于 2025-3-26 06:14:08 | 只看該作者
Weakly Stationary Processes and Their Spectral Measures,Stationary stochastic processes are analyzed at the level of their first and second order characteristics, mean and covariance, using Fourier methods.
28#
發(fā)表于 2025-3-26 09:19:12 | 只看該作者
29#
發(fā)表于 2025-3-26 16:19:02 | 只看該作者
,Birkhoff’s Ergodic Theorem,In the context of stochastic processes, ergodic theory relates the long-run “time-averages” such as the sample mean of an evolving strictly stationary process .., .., … to a “phase-average” computed as an expected value with respect to a probability distribution on the state space. This is the perspective developed in this chapter.
30#
發(fā)表于 2025-3-26 19:04:35 | 只看該作者
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