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We found that it took much longer for this parameter to converge than is typical for other parameters in Structure (pers. obs)., so each run was made with 1 million sample generations after 1 million generations of burn-in.
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Two reproducible sample generation approaches are proposed in this research.
The 1/f ambient noise sample generation is based on transformation functions performed on uniform random sequences.
This method relies on a technique for random sample generation in a given domain.
The proposed approach firstly applies reproducible sample generation to convert the observational data to experimental data.
The sample generation is then based on the conditional densities method.
The process of sample generation in the framework of the Markov chain is superimposed on Figure 10.a3 and Figure 10.a4.
This way, we avoid the step of sample generation and evaluation, and thus the processing load is dramatically decreased.
OG and CS contributed to sample generation.
In this study, cautions were made to ensure consistency in image sample generation.
The electoral list, used for random sample generation in Sri Lanka in earlier studies, provided the sample frame [ 8, 9].
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