ExclusiveKL

class viabel.ExclusiveKL(approx, model, num_mc_samples, use_path_deriv=False, hessian_approx_method=None)

Exclusive Kullback-Leibler divergence.

Equivalent to using the canonical evidence lower bound (ELBO)

with reparameterized gradient estimator and control variate

This implementation of reparameterization and control variate is based on:

“Reducing Reparameterization Gradient Variance” by Andrew C. Miller, Nicholas J. Foti , Alexander D’Amour , and Ryan P. Adams, Code based on the implementation by Andrew C. Miller: https://github.com/andymiller/ReducedVarianceReparamGradients

Attributes:

approx

The approximation family.

model

The model.

num_mc_samples

Number of Monte Carlo samples to use to approximate the objective.

Methods

__call__(var_param)	Evaluate objective and its gradient.
update(var_param, direction)	Update the variational parameter in optimization.

__init__(approx, model, num_mc_samples, use_path_deriv=False, hessian_approx_method=None)

Parameters:

approxApproximationFamily object

modelModel object

num_mc_sampleint

Number of Monte Carlo samples to use to approximate the objective.

use_path_derivbool

Use path derivative (for “sticking the landing”) gradient estimator

hessian_approx_method‘string’

Select from different methods for approximating the hessian:

‘full’ : use the full hessian matrix provided by BridgeStan ‘mean_only’ : use control variate only for mean estimator to avoid calculation of full hessian ‘loo_diag_approx’ : using “leave one out” method with hessian vector product value at other samples to

estimate the diagonal values of hessian

‘loo_direct_approx;: the same method as ‘loo_diag_approx’ but use the scaled approximation to the

gradient of scale to do the “loo” estimation