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Unlike standard finite element methods, our discretization scheme relies on macro- and microfinite elements.
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The relationships capable of being described by our discretization method add biologically relevant information not available by correlation analysis.
In our method, the discretization is performed automatically by fitting abundance levels of both case and control populations to a Gaussian mixture model and estimates the parameters using an Expectation-Maximization algorithm.
There are several known methods for discretization including Equal Information Gain, Maximum Entropy, and Equal Interval Width.
We have previously described methods for discretization of continuous data and their potential impact on learned networks during structure learning [ 8, 20].
To be able to fully exploit our MTU particle filter method, the discretization stepsize must be chosen appropriately.
However, our proposed discretization method here is an approximation for the right-hand side as it is pretty clear from formula (8).
The existence proofs are constructive: they are based on Rothe's method (semi- discretization in time), and on our generalization of the Lions-Aubin-Simon's compactness lemma to moving boundary problems.
Covers advanced topics in numerical methods for the discretization, solution, and control of problems governed by partial differential equations.
The spectral methods are essentially discretization methods for the approximate solution of partial differential equations.
Because of the intrinsic differences between these methods, discretization leads to different biases of the calculated spike time PDFs.
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