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Hence, we developed a general-purpose Hidden Markov Model (HMM) ChIP-seq simulator that can generate sample reads at user-defined emission rates from specified genomic loci.
Protection mode (no air pumped, device closure, and filters protected) was activated if wind conditions could generate sample contamination.
Other electronic instruments use digital technology to generate, sample, and manipulate wind instrument acoustics.
Numerical simulation was utilized to generate sample paths fitting the residence time distributions (RTD) of the system.
In order to save the computational cost, an optimal sampling strategy is applied to generate sample points at the stage of design of experiment (DOE).
On the other hand, the widespread use of a guaranteed show-up fee that is non-stochastic may generate sample selection biases of the opposite direction, encouraging more risk averse samples into experiments.
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The general purpose software allows to generate samples from Gaussian processes only.
The best-candidate algorithm is an approach to generate sampling patterns mimic the Poisson disk patterns.
Markov chain Monte Carlo algorithms can generate samples from these posterior distributions, but creating reasonably efficient sampling algorithms requires advanced techniques.
this will not synchronize to the audio card, and will generate samples as fast as it can.
Uniformity sampling is adopted to generate samples which will be used to establish surrogate model.
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