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To estimate the sensitivity of the detection pipeline and infer the total number of pSNMs in clinically unremarkable individuals, we performed a computer simulation to generate ∼20 000 simulated mosaic sites each at varied minor allele fractions by mixing the real sequencing data (see Materials and Methods).
Our work in the first place employs time domain simulation to generate massive disturbed scenarios using the published fault simulator.
The platform uses Monte Carlo Simulation to generate results with statistical significance.
A heavy summer loading case of the Western Electricity Coordinating Council (WECC) is used in simulation to generate PMU data.
The method utilizes the adaptive Markov chain simulation to generate samples that can adaptively populate the important region.
In this paper, an evolutionary algorithm is coupled with a constraint-based discrete-event simulation to generate feasible schedules based on priority lists.
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We then used a simple statistical simulation model to generate the results of serological surveys.
The authors designed a complex simulation model to generate 'realistic' copy-number profiles.
Instead, it uses a probabilistic simulation model to generate data sets to compare with the empirical data.
Tests were performed using 100 simulated demes and 50,000 coalescent simulations to generate a null distribution under neutral expectations around observed FST values (with confidence intervals).
We add the variation effects to the nominal HSPICE model of STT-RAM and use Monte Carlo simulations to generate the error rates caused by each variation.
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