estimation - MSM estimation from data (`pyemma.msm.estimation`)¶

Countmatrix¶

`count_matrix`(dtraj, lag[, sliding, ...])	Generate a count matrix from given microstate trajectory.
`cmatrix`(dtraj, lag[, sliding, ...])	Generate a count matrix from given microstate trajectory.

`connected_sets`(C[, directed])	Compute connected sets of microstates.
`largest_connected_set`(C[, directed])	Largest connected component for a directed graph with edge-weights given by the count matrix.
`largest_connected_submatrix`(C[, directed, lcc])	Compute the count matrix on the largest connected set.
`connected_cmatrix`(C[, directed, lcc])	Compute the count matrix on the largest connected set.
`is_connected`(C[, directed])	Check connectivity of the given matrix.

`transition_matrix`(C[, reversible, mu])	Estimate the transition matrix from the given countmatrix.
`tmatrix`(C[, reversible, mu])	Estimate the transition matrix from the given countmatrix.
`log_likelihood`(C, T)	Log-likelihood of the count matrix given a transition matrix.
`tmatrix_cov`(C[, k])	Covariance tensor for non-reversible transition matrix posterior.
`error_perturbation`(C, S)	Error perturbation for given sensitivity matrix.

tmatrix_sampler(C[, reversible, mu, T0]) Generate transition matrix sampler object.

`bootstrap_counts`(dtrajs, lagtime)	Generates a randomly resampled count matrix given the input coordinates.
`bootstrap_trajectories`(trajs, correlation_length)	Generates a randomly resampled trajectory segments.

`prior_neighbor`(C[, alpha])	Neighbor prior for the given count matrix.
`prior_const`(C[, alpha])	Constant prior for given count matrix.
`prior_rev`(C[, alpha])	Prior counts for sampling of reversible transition matrices.