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Exact(23)
Their mutual dependence can be defined as MI ( x, y ) = E x, y log 2 p ( y | x ) p ( y ), where Ex,y is the expectation over {x,y}, p y|x) is the conditional probability density function (PDF) of y given x, and p y) is the marginal PDF of y.
Mutual information can be calculated in the form of KL divergence KL in (5), where p y denotes the probability density function (PDF) of a random vector y, p y n denotes the n th marginal PDF of y, and z is a dummy variable for the integral [16].
This model employs a presumed probability density function (PDF), in which the marginal PDF of a reactive scalar is modeled by a statistically most likely distribution.
It is further shown that the marginal PDF of the volatile mixture fraction is not well approximated by a beta function PDF in regions of rapid devolatilization.
The marginal pdf of the progress variable is presumed to be β-pdf and the pdf of the conditional dissipation rate is taken to be log-normal.
Mixture-fraction-conditioned data, conditional PDFs, and burning indices are computed and compared with the delta-function flamelet closure model, which employs a Dirac distribution as a model for the marginal PDF of the reaction progress parameter.
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However, since the expression of the joint PDF is very complicated, no simple closed-form expressions have been found for the marginal PDFs of the ordered eigenvalues, although some computable expressions have been found in [70].
Marginal PDFs of mixture fraction, enthalpy, and gas temperature are presented.
Integrating (1) with respect to or lead to the marginal PDFs of the phase and magnitude as follows: (2).
Therefore, we use an approximate inference framework to compute the estimates of the marginal pdfs of the variables, called beliefs.
Marginal PDFs of selected CMT parameters (marginal histograms) are fitted with Gaussian function defined by its mean and standard deviation σ.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com