bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison¶

class bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison(model_dict, bayes_opts, perturbed_data=None, n_bootstrap=1000, data_noise_level=0.01, use_emulator=True, out_dir='Outputs_Comparison/')¶

Bases: object

A class to perform Bayesian Analysis.

Attributes¶

model_dictdict: A dictionary of model names and bvr.engine objects for each model.
bayes_optsdict: A dictionary given the BayesInference options.
perturbed_dataarray of shape (n_bootstrap_itrs, n_obs), optional: User defined perturbed data. The default is None.
n_bootstrapint: Number of bootstrap iteration. The default is 1000.
data_noise_levelfloat: A noise level to perturb the data set. The default is 0.01.
use_emulatorbool: Set to True if the emulator/metamodel should be used in the analysis. If False, the model is run.
out_dirstring, optional: Name of output folder for the generated plots. The default is ‘Outputs_Comparison/’.

__init__(model_dict, bayes_opts, perturbed_data=None, n_bootstrap=1000, data_noise_level=0.01, use_emulator=True, out_dir='Outputs_Comparison/')¶

Methods

`__init__`(model_dict, bayes_opts[, ...])
`calc_bayes_factors`()	Calculate the BayesFactors for each pair of models in the model_dict with respect to given data.
`calc_justifiability_analysis`()	Perform justifiability analysis by calculating the confusion matrix
`calc_model_weights`()	Calculate the model weights from BME evaluations for Bayes factors.
`generate_ja_dataset`()	Generates the data set for the justifiability analysis.
`model_comparison_all`()	Performs all three types of model comparison:
`plot_bayes_factor`(bme_dict)	Plots the Bayes factor distibutions in a \(N_m \times N_m\) matrix, where \(N_m\) is the number of the models.
`plot_confusion_matrix`(confusion_matrix)	Visualizes the confusion matrix and the model weights for the justifiability analysis.
`plot_model_weights`(model_weights)	Visualizes the model weights resulting from BMS via the observation data.
`setup`()	Initialize parameters that are needed for all types of model comparison

calc_bayes_factors() → dict¶

Calculate the BayesFactors for each pair of models in the model_dict with respect to given data.

Returns¶

bme_dictdict: The calculated BME values for each model

calc_justifiability_analysis() → dict¶

Perform justifiability analysis by calculating the confusion matrix

Returns¶

confusion_matrix: dict: The averaged confusion matrix.

calc_model_weights() → dict¶

Calculate the model weights from BME evaluations for Bayes factors.

Returns¶

model_weightsdict: The calculated weights for each model

generate_ja_dataset() → tuple[list, list, object]¶

Generates the data set for the justifiability analysis.

Returns¶

just_list: list: List of the model outputs for each of the given models, as well as the perturbed observations.
var_listlist: List of the uncertainty/stdev associated with each model output and perturbed observation.
samplerobject: bvr.PostSampler object.

model_comparison_all() → dict¶

Performs all three types of model comparison:

Bayes Factors
Model weights
Justifiability analysis

Returns¶

resultsdict: A dictionary that contains the calculated BME values, model weights and confusion matrix

plot_bayes_factor(bme_dict)¶

Plots the Bayes factor distibutions in a \(N_m \times N_m\) matrix, where \(N_m\) is the number of the models.

Parameters¶

bme_dictdict: A dictionary containing the BME values of the models.

plot_confusion_matrix(confusion_matrix)¶

Visualizes the confusion matrix and the model weights for the justifiability analysis.

Parameters¶

confusion_matrix: dict: The averaged confusion matrix.

plot_model_weights(model_weights)¶

Visualizes the model weights resulting from BMS via the observation data.

Parameters¶

model_weightsarray: Model weights.

setup()¶: Initialize parameters that are needed for all types of model comparison