pyemma.msm.estimation.bootstrap_counts¶

pyemma.msm.estimation.bootstrap_counts(dtrajs, lagtime)¶

Generates a randomly resampled count matrix given the input coordinates.

Parameters:	dtrajs (array-like or array-like of array-like) – single or multiple discrete trajectories. Every trajectory is assumed to be a statistically independent realization. Note that this is often not true and is a weakness with the present bootstrapping approach. lagtime (int) – the lag time at which the count matrix will be evaluated

Notes

This function can be called multiple times in order to generate randomly resampled realizations of count matrices. For each of these realizations you can estimate a transition matrix, and from each of them computing the observables of your interest. The standard deviation of such a sample of the observable is a model for the standard error.

The bootstrap will be generated by sampling N/lagtime counts at time tuples (t, t+lagtime), where t is uniformly sampled over all trajectory time frames in [0,n_i-lagtime]. Here, n_i is the length of trajectory i and N = sum_i n_i is the total number of frames.