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A workflow is presented to create surrogate models from any or all of the properties which affect production.
This algorithm exploits stochastic expansions derived from the Non-Intrusive Polynomial Chaos (NIPC) technique to create surrogate models utilized in the optimization process.
We thus investigate several methods of down-sampling the co-presence signal in order to create surrogate contact networks, in the spirit of [28, 29], and compare these surrogate data to the actual networks of face-to-face contacts.
We have thus examined several methods to downsample the co-presence networks to create surrogate contact networks with overall the same amount of contact time than the real contact data.
The chance level was estimated by shuffling the spike times (1000 repetitions) to create surrogate curves y t o n k (t i ).
However, the method is fundamentally different from that of Kong et al. [ 11], because it is not iterative, does not create surrogate family members, works with haplotypes instead of genotypes, and searches for short shared haplotypes as well as long ones.
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Growing networks with specified characters (degree distribution and modularity) provides the opportunity to create surrogates for biological and technological networks, and to test hypotheses about the processes that gave rise to them.
An alternative approach based on conceptual modelling for creating surrogate models is presented.
The Poisson simulation creates surrogate traces by stimulating a single model neuron with synaptic inputs.
Mokeichev et al. (2007) [21] proposed three new methods for creating surrogate data of intracellular traces, and their results did not reject the null hypothesis that repeats of cortical activity are stochastically generated.
To examine whether this distribution was within possible stochastic fluctuations, we created surrogate data by randomly shuffling the rank order of spine size in the each pooled dataset.
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CEO of Professional Science Editing for Scientists @ prosciediting.com