bayesvalidrox.bayes_inference.post_sampler.PostSampler¶
- class bayesvalidrox.bayes_inference.post_sampler.PostSampler(engine=None, discrepancy=None, observation=None, out_names=None, selected_indices=None, use_emulator=False, out_dir='')¶
Bases:
objectTemplate class for generating posterior samples. This class describes all the properties and functions that are needed to interface with the class BayesInference.
Attributes¶
- engineobject, optional
Trained bvr.Engine object. The default is None.
- discrepancyobject, optional
Object of class bvr.Discrepancy. The default is None.
- observationdict, optional
Measurement/observation to use as reference. The default is None.
- out_nameslist, optional
The list of requested output keys to be used for the analysis. The default is None. If None, all the defined outputs from the engine are used.
- selected_indicesdict, optional
A dictionary with the selected indices of each model output. The default is None. If None, all measurement points are used in the analysis.
- use_emulatorbool
Set to True if the emulator/metamodel should be used in the analysis. If False, the model is run.
- out_dirstring, optional
The output directory. The default is ‘’.
- __init__(engine=None, discrepancy=None, observation=None, out_names=None, selected_indices=None, use_emulator=False, out_dir='')¶
Methods
__init__([engine, discrepancy, observation, ...])calculate_loglik_logbme(model_evals[, ...])Calculate log-likelihoods and logbme on the perturbed data.
normpdf(outputs[, std_outputs, rmse])Calculates the likelihood of simulation outputs compared with observation data.
Performs sampling to update the prior distribution on the input parameters.
- calculate_loglik_logbme(model_evals, surr_error=None, std_outputs=None) tuple[ndarray, ndarray]¶
Calculate log-likelihoods and logbme on the perturbed data. This function assumes everything as Gaussian.
Parameters¶
- model_evalsdict
Model or metamodel outputs as a dictionary.
- surr_errordict, optional
A dictionary containing the root mean squared error as array of shape (n_samples, n_measurement) for each model output. The default is None.
- std_outputsdict of 2d np arrays, optional
Standard deviation (uncertainty) associated to the output. The default is None.
Returns¶
- log_likelihoodnp.ndarray
The calculated loglikelihoods. Size: (n_samples, n_bootstrap_itr).
- log_bmenp.ndarray
The log bme. This also accounts for metamodel error, if self.use_emulator is True. Size: (1,n_bootstrap_itr).
- normpdf(outputs, std_outputs=None, rmse=None) ndarray¶
Calculates the likelihood of simulation outputs compared with observation data.
Parameters¶
- outputsdict
The metamodel outputs as an array of shape (n_samples, n_measurement) for each model output.
- std_outputsdict of 2d np arrays, optional
Standard deviation (uncertainty) associated to the output. The default is None.
- rmsedict, optional
A dictionary containing the root mean squared error as array of shape (n_samples, n_measurement) for each model output. The default is None.
Returns¶
- logLiknp.ndarray
Log-likelihoods. Shape: (n_samples)