Markov State Models package (pyemma.msm)¶
The msm package provides functions to estimate, analyze and generate discrete-state Markov models. All public functions accept dense NumPy and sparse SciPy matrices and automatically choose the corresponding implementation.
User Functions¶
For most users, the following high-level functions are sufficient to estimate msm models from data. Expert users may want to construct Estimators or Models (see below) directly.
markov_model(P[, dt_model]) |
Markov model with a given transition matrix |
timescales_msm(dtrajs[, lags, nits, ...]) |
Implied timescales from Markov state models estimated at a series of lag times. |
its(dtrajs[, lags, nits, reversible, ...]) |
Implied timescales from Markov state models estimated at a series of lag times. |
estimate_markov_model(dtrajs, lag[, ...]) |
Estimates a Markov model from discrete trajectories |
bayesian_markov_model(dtrajs, lag[, ...]) |
Bayesian Markov model estimate using Gibbs sampling of the posterior |
tpt(msmobj, A, B) |
A->B reactive flux from transition path theory (TPT) |
timescales_hmsm(dtrajs, nstates[, lags, ...]) |
Calculate implied timescales from Hidden Markov state models estimated at a series of lag times. |
estimate_hidden_markov_model(dtrajs, ...[, ...]) |
Estimates a Hidden Markov state model from discrete trajectories |
bayesian_hidden_markov_model(dtrajs, ...[, ...]) |
Bayesian Hidden Markov model estimate using Gibbs sampling of the posterior |
MSM classes¶
Estimators to generate models from data. If you are not an expert user, use the API functions above.
ImpliedTimescales(estimator[, lags, nits, ...]) |
Implied timescales for a series of lag times. |
ChapmanKolmogorovValidator(model, estimator, ...) |
|
MaximumLikelihoodMSM([lag, reversible, ...]) |
Maximum likelihood estimator for MSMs given discrete trajectory statistics |
BayesianMSM([lag, nsamples, nsteps, ...]) |
Bayesian Markov state model estimator |
OOMReweightedMSM([lag, reversible, ...]) |
OOM based estimator for MSMs given discrete trajectory statistics |
MaximumLikelihoodHMSM([nstates, lag, ...]) |
Maximum likelihood estimator for a Hidden MSM given a MSM |
BayesianHMSM([nstates, lag, stride, ...]) |
Estimator for a Bayesian Hidden Markov state model |
Models of the kinetics or stationary properties of the data. If you are not an expert user, use the API functions above.
MSM(P[, pi, reversible, dt_model, neig, ncv]) |
Markov model with a given transition matrix |
SampledMSM(P[, pi, reversible, dt_model, ...]) |
Sampled Markov state model |
HMSM(P, pobs[, pi, dt_model]) |
Hidden Markov model on discrete states. |
SampledHMSM(P, pobs[, pi, dt_model]) |
Sampled Hidden Markov state model |
ReactiveFlux(A, B, flux[, mu, qminus, ...]) |
A->B reactive flux from transition path theory (TPT) |
PCCA(P, m) |
PCCA+ spectral clustering method with optimized memberships [1]_ |
MSM functions (low-level API)¶
Low-level functions for estimation and analysis of transition matrices and io have been moved to MSMTools.