SBART.Samplers.SbartBaseSampler

Common interface of the SBART samplers.

Note: Not supposed to be used by the user!

Classes

SbartBaseSampler

Base semi-Bayesian sampler, which implements the SBART model as described in the paper.

class SbartBaseSampler

Bases: SamplerModel

Base semi-Bayesian sampler, which implements the SBART model as described in the paper.

The posterior characterization algorithms inherit from this:

• Laplace’s approximation: Laplace_approx

• MCMC: MCMC_sampler

__init__(mode, RV_step, RV_window, user_configs, sampler_folders=None)

Approximate the posterior distribution with a LaPlace approximation;

optimize_orderwise(target, target_kwargs)

Optimization over the functions that implements the orde-rwise application. This must be implemented by the children classes, as each model will use a different optimization strategy

Parameters:

• target ([type]) – [description]

• target_kwargs ([type]) –

  Input arguments of the target function. Must contain the following:

  • dataClassProxy,

  • frameID

  • order

Returns:

[description]

Return type:

[type]

optimize_epochwise(target, target_kwargs)

optimize(target, target_kwargs)

Compute the RV for an entire order, followed by a parabolic fit to estimate uncertainty and better adjust chosen RV

Parameters:

• target ([type]) – [description]

• target_kwargs ([type]) –

  Input arguments of the target function. Must contain the following:

  • dataClassProxy,

  • frameID

  • order

Returns:

[description]

Return type:

[type]

process_epochwise_metrics(outputs)

Each children class must implement this, as it will be used to parse the outputs when the optimal RV is provided to the target!

Parameters:

outputs –

compute_epochwise_combination(outputs)

Each children class must implement this to combine the order-wise metrics into a “global” value for the optimization process :param outputs:

show_posterior(mean_value, variance, RVs)

Plot the approximated (Gaussian) posterior