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Ensemble-averaged correlation function stochastic models

Since these characteristics are time-dependent, let us assume particle birth-death and migration to be the Markov stochastic processes. Note that making use of the stochastic models, we discuss below in detail, does not contradict the deterministic equations employed for these processes. Say, the equations for nv t), Xu(r,t), Y(r,t) given in Section 2.3.1 are deterministic since both the concentrations and joint correlation functions are defined by equations (2.3.2), (2.3.4) just as ensemble average quantities. Note that the... [Pg.115]

SA of SODEs describing chemically reacting systems was introduced early on, in the case of white noise added to an ODE (Dacol and Rabitz, 1984). In addition to expected values (time or ensemble average quantities), SA of variances or other correlation functions, or even the entire pdf, may also be of interest. In other words, in stochastic or multiscale systems one may also be interested in identifying model parameters that mostly affect the variance of different responses. In many experimental systems, the noise is due to multiple sources as a result, comparison with model-based SA for parameter estimation needs identification of the sources of experimental noise for meaningful conclusions. [Pg.47]


See other pages where Ensemble-averaged correlation function stochastic models is mentioned: [Pg.584]    [Pg.742]    [Pg.110]    [Pg.110]    [Pg.328]    [Pg.251]    [Pg.341]    [Pg.110]   
See also in sourсe #XX -- [ Pg.336 ]

See also in sourсe #XX -- [ Pg.336 ]




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Average correlation

Average function

Average functionality

Averaged Models

Averages ensemble average

Correlated models

Correlation function models

Correlation functions averages

Correlation models

Ensemble average

Ensemble averaging

Ensemble modeling

Ensemble-averaged correlation function

Functional modeling

Functional models

Model function

Modeling Correlation

Stochastic function

Stochastic modeling

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