threeML.bayesian.bayesian_analysis module

class threeML.bayesian.bayesian_analysis.BayesianAnalysis(likelihood_model: Model, data_list: DataList, **kwargs)[source]

Bases: object

property analysis_type: str

convergence_plots(n_samples_in_each_subset, n_subsets)[source]

Compute the mean and variance for subsets of the samples, and plot them. They should all be around the same values if the MCMC has converged to the posterior distribution.

The subsamples are taken with two different strategies: the first is to slide a fixed-size window, the second is to take random samples from the chain (bootstrap)

Parameters:

n_samples_in_each_subset – number of samples in each subset
n_subsets – number of subsets to take for each strategy

Returns:

a matplotlib.figure instance

property data_list: DataList

data list for this analysis

Type:: return

property likelihood_model: Model

likelihood model (a Model instance)

Type:: return

property log_like_values: ndarray | None

Returns the value of the log_likelihood found by the bayesian sampler while sampling from the posterior. If you need to find the values of the parameters which generated a given value of the log. likelihood, remember that the samples accessible through the property .raw_samples are ordered in the same way as the vector returned by this method.

Returns:: a vector of log. like values

property log_marginal_likelihood: float | None

Return the log marginal likelihood (evidence ) if computed

return:

property log_probability_values: ndarray | None

Returns the value of the log_probability (posterior) found by the bayesian sampler while sampling from the posterior. If you need to find the values of the parameters which generated a given value of the log. likelihood, remember that the samples accessible through the property .raw_samples are ordered in the same way as the vector returned by this method.

Returns:: a vector of log probabilty values

plot_chains(thin=None)[source]

Produce a plot of the series of samples for each parameter

Parameters:: thin – use only one sample every ‘thin’ samples
Returns:: a matplotlib.figure instance

property raw_samples: ndarray | None

Access the samples from the posterior distribution generated by the selected sampler in raw form (i.e., in the format returned by the sampler)

Returns:: the samples as returned by the sampler

restore_MAP_fit() → None[source]: Sets the model parameters to the MAP of the probability

restore_median_fit()[source]: Sets the model parameters to the mean of the marginal distributions

property results: BayesianResults | None

sample(quiet=False) → None[source]

sample the posterior of the model with the selected algorithm

If no algorithm as been set, then the configured default algorithm we default parameters will be run

Parameters:: quiet – if True, then no output is displayed
Returns:

property sampler: SamplerBase | None: Access the instance of the sampler used to sample the posterior distribution :return: an instance of the sampler

property samples: Dict[str, ndarray] | None

Access the samples from the posterior distribution generated by the selected sampler

Returns:: a dictionary with the samples from the posterior distribution for each parameter

set_sampler(sampler_name: str = 'default', **kwargs)[source]

Set the sampler :param sampler_name: (str) Name of sampler

Parameters:: share_spectrum – (optional) Option to share the spectrum calc

between detectors with the same input energy bins