autoemulate.calibration.bayes

autoemulate.calibration.bayes#

class BayesianCalibration(emulator, parameter_range, observations, observation_noise=0.01, model_uncertainty=False, model_discrepancy=0.0, calibration_params=None, device=None, log_level='progress_bar')[source]#

Bases: TorchDeviceMixin, BayesianMixin

Bayesian calibration using Markov Chain Monte Carlo (MCMC).

Bayesian calibration estimates the probability distribution over input parameters given observed data, providing uncertainty estimates.

model(predict=False)[source]#

Pyro model.

Parameters:

predict (bool) – Whether to run the model with existing samples to generate posterior predictive distribution. Used with pyro.infer.Predictive.

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