class BayesianMSMPosterior

class deeptime.markov.msm.BayesianMSMPosterior(prior=None, samples=None)

Bayesian posterior from bayesian MSM sampling.

Parameters:

See also

deeptime.markov.msm.BayesianMSM

Attributes

lagtime

Lagtime of the models.

prior

The prior model.

samples

The sampled models

Methods

ck_test(models, n_metastable_sets[, ...])

Performs a Chapman Kolmogorov test.

copy()

Makes a deep copy of this model.

evaluate_samples(quantity[, delimiter])

Obtains a quantity (like an attribute or result of a method or a property) from each of the samples.

gather_stats(quantity[, store_samples, ...])

Obtain statistics about a sampled quantity.

get_params([deep])

Get the parameters.

set_params(**params)

Set the parameters of this estimator.

submodel(states)

Creates a bayesian posterior that is restricted onto the specified states.

timescales([k])

Relaxation timescales corresponding to the eigenvalues.
ck_test(models, n_metastable_sets, include_lag0=True, err_est=False, progress=None)

Performs a Chapman Kolmogorov test. See MarkovStateModel.ck_test for more details

copy() → Model

Makes a deep copy of this model.

Returns:

A new copy of this model.

Return type:

copy

evaluate_samples(quantity, delimiter='/', *args, **kwargs)

Obtains a quantity (like an attribute or result of a method or a property) from each of the samples. Returns as list.

Parameters:

  • quantity (str) – The quantity. Can be also deeper in the instance hierarchy, indicated by the delimiter.

  • delimiter (str, default='/') – The delimiter.

  • *args – Arguments passed to the evaluation point of the quantity.

  • **kwargs – Keyword arguments passed to the evaluation point of the quantity.

Returns:

result – A list of the quantity evaluated on each of the samples. If can be converted to float ndarray then ndarray.

Return type:

list of any or ndarray

gather_stats(quantity, store_samples=False, delimiter='/', confidence=0.95, *args, **kwargs)

Obtain statistics about a sampled quantity. Can also be a chained call, separated by the delimiter.

Parameters:

  • quantity (str) – name of attribute, which will be evaluated on samples

  • store_samples (bool, optional, default=False) – whether to store the samples (array).

  • delimiter (str, optional, default='/') – separator to call members of members

  • confidence (float, optional, default=0.95) – Size of the confidence intervals.

  • *args – pass-through

  • **kwargs – pass-through

Returns:

statistics – The statistics

Return type:

deeptime.util.QuantityStatistics

get_params(deep=False)

Get the parameters.

Returns:

params – Parameter names mapped to their values.

Return type:

mapping of string to any

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

object

submodel(states: ndarray)

Creates a bayesian posterior that is restricted onto the specified states.

Parameters:

states ((N,) ndarray, dtype=int) – array of integers specifying the states to restrict to

Returns:

submodel – A posterior with prior and samples restricted to specified states.

Return type:

BayesianMSMPosterior

timescales(k=None)

Relaxation timescales corresponding to the eigenvalues.

Parameters:

k (int, optional, default=None) – The number of timescales (excluding the stationary process).

Returns:

timescales – Timescales of the prior and timescales of the samples.

Return type:

tuple(iterable, iterable)

property lagtime

Lagtime of the models.

property prior

The prior model.

Returns:

prior – the prior

Return type:

deeptime.markov.msm.MarkovStateModel or None

property samples

The sampled models

Returns:

models – samples

Return type:

list of deeptime.markov.msm.MarkovStateModel or None