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Consider two independent chains of samples, for example, and.
The large soluble fraction and long pendent chains of samples showing the lowest crosslink densities were responsible for their poor elastic recovery.
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The Markov chain of samples at time k is generated as follows.
Because this is a simulation-based approach, we repeated many draws or iterations and evaluated whether the chain of sample values converged to a stable distribution, which was assumed to be the posterior distribution in which we are interested.
In total six chains of 50 000 samples were generated using hCSI-MH, with 10 000 samples discarded for burn-in in each run.
Inspired from this, Mao and Yin [19] opted for chains of tracked samples (tracklets) to automatically label target data.
We elucidated and semiquantified nearly 300 of oligosaccharide chains of LMWH samples using this method.
All the Bayesian analyses were performed using 3 chains of 50000 samples, out of which the first 25000 were removed to allow for convergence.
We generated 4 separate Markov chains of 10,000 samples each, starting from the best-fit control parameters with a burnin of 100.
> -wrap-foot> In all experiments, six chains of 100 000 samples were generated for hCSI-Gibbs and hCSI-MH discarding 10 000 each for burn-in, with three chains of 200 000 generated for CSI-Gibbs, allowing 20 000 steps for burn-in.
Comparison of the size distributions of the less sulfated domains (Figure 3C) for 100 chains of each sample shows a larger fraction of shorter domains (below 10 disaccharides) for epithelial HS chains and a larger fraction of longer domains (above 20 disaccharides) for fibroblast HS chains.
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