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The use of random fields and Monte-Carlo simulations deliver a mean and standard deviation of the elastic parameters that lead naturally to a probabilistic interpretation.
The paper describes the use of random fields and finite elements to assess the influence of porosity and void size on the effective elastic stiffness of geomaterials.
The use of random fields increases the dimensionality of the stochastic domain, which poses several computational challenges related to the minimization of the Excess Probability as a measure of risk awareness.
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Cellular SA-βGal activity was quantitated using 8 10 images taken of random fields from each sample by fluorescence microscopy.
Images of random fields were taken using a Nikon Eclipse 80 i fluorescence microscope at 200× magnification.
It is based on a discretization technique of random fields that was established using the Karhunen Loeve procedure.
A discriminative approach that overcomes this difficulty is the use of conditional random fields (CRFs).
This is demonstrated in this work by random geometrical perturbations described with the use of Gaussian random fields.
Moreover, we investigated the use of Markov random field models to introduce spatial coherence in the final classification and show that the two types of context models can be integrated successfully.
The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%.
In this work, we discuss relevant aspects concerning the use of discrete Markov random fields (MRF) in the simulation of rock properties in petroleum reservoirs.
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