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Titlebook: Cycle Representations of Markov Processes; Sophia L. Kalpazidou Book 19951st edition Springer Science+Business Media New York 1995 Markov

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31#
發(fā)表于 2025-3-27 00:37:44 | 只看該作者
Historical Review of Advance DirectivesThis chapter deals with the cycle generating equations defined by the transition probabilities of denumerable Markov chains ξ which are recurrent. The solutions (?, ..) of cycles and weights to these equations will be called . of ξ.
32#
發(fā)表于 2025-3-27 02:40:43 | 只看該作者
33#
發(fā)表于 2025-3-27 09:17:44 | 只看該作者
Advance in Structural BioinformaticsPaul Lévy investigated “the allure” of the sample paths of general Markov processes ξ = {ξ.}. with denumerable state space . by using the properties of the so-called .-intervals, that is the sets .(. = {.: ξ. = .}). Lévy’s study concludes with a very fine property of the transition probabilities ... of ξ known as the .:
34#
發(fā)表于 2025-3-27 10:10:19 | 只看該作者
35#
發(fā)表于 2025-3-27 17:25:01 | 只看該作者
36#
發(fā)表于 2025-3-27 18:26:03 | 只看該作者
37#
發(fā)表于 2025-3-27 22:51:36 | 只看該作者
Stochastic Properties in Terms of CircuitsIn the present chapter we shall be concerned with circuit Markov chains and we shall investigate their recurrent behavior, entropy production and reversibility property. The principal results will give criterions in terms of the representative circuits and weights.
38#
發(fā)表于 2025-3-28 06:06:34 | 只看該作者
Lévy’s Theorem Concerning Positiveness of Transition ProbabilitiesPaul Lévy investigated “the allure” of the sample paths of general Markov processes ξ = {ξ.}. with denumerable state space . by using the properties of the so-called .-intervals, that is the sets .(. = {.: ξ. = .}). Lévy’s study concludes with a very fine property of the transition probabilities ... of ξ known as the .:
39#
發(fā)表于 2025-3-28 09:33:50 | 只看該作者
40#
發(fā)表于 2025-3-28 14:25:00 | 只看該作者
Textbook 2005odels covered include vector autoregressive, cointegrated,vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood and Bayesian methods are considered for estimating these models
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