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Geostatistics science provides a number of methods based on random functions theory, which are frequently used to estimate the value of a spatially measured variable at the unsampled places.
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The number of radiation tracks in each cell is calculated based on random function, and the absorbed dose is calculated as the accumulation of the radiation tracks.
A formulation based on random variables and probabilistic functions was developed, which allows prediction of the storage space requirement without requiring fully-integrated simulation models.
Three different model structures are considered: one without a detachment process and with Langmuirian blocking function, one with detachment, and one with detachment and a second-order blocking function based on random sequential adsorption.
A regret function is defined, and minimization of the maximum regret provides an objective function based on random future usage stresses.
One of the important applications is to estimate an unknown density function based on random samples [1 3].
We describe a number of algorithms that are based on random forest to estimate the conditional average treatment effect (CATE) function and we compare them using theoretical results under a simple causal model and simulation studies.
If the value of the function is larger than what would be expected based on random spacing, the spatial pattern is clustered; if smaller, it is systematic.
First, the formulas for eigenvalue and eigenvector, drift PSD functions, drift spectral moments, drift reliabilities, their first and second derivatives are derived based on random vibration theory.
Generalities based on random observation will get you into trouble.
A measure of betweenness centrality based on random walks.
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