API

Convenience Methods

bbvi(dimension, *[, n_iters, …])	Fit a model using black-box variational inference.
vi_diagnostics(var_param, *[, objective, …])	Check variational inference diagnostics.

Approximation Families

ApproximationFamily(dim, var_param_dim, …)	An abstract class for an variational approximation family.
MFGaussian(dim[, seed])	A mean-field Gaussian approximation family.
MFStudentT(dim, df[, seed])	A mean-field Student’s t approximation family.
MultivariateT(dim, df[, seed])	A full-rank multivariate t approximation family.

Models

Model(log_density)	Base class for representing a model.
StanModel(fit)	Class that encapsulates a PyStan model.

Variational Objectives

VariationalObjective(approx, model)	A class representing a variational objective to minimize
ExclusiveKL(approx, model, num_mc_samples)	Exclusive Kullback-Leibler divergence.
AlphaDivergence(approx, model, …)	Log of the alpha-divergence.

Diagnostics

all_diagnostics(log_weights, *[, samples, …])	Compute all VI diagnostics.
divergence_bound(log_weights, *[, alpha, …])	Compute a bound on the alpha-divergence.
error_bounds(*[, W1, W2, q_var, p_var])	Compute error bounds.
wasserstein_bounds(d2, *[, samples, …])	Compute all bounds.

Optimization

Optimizer()	An abstract class for optimization
StochasticGradientOptimizer(learning_rate)	An abstract class of descent direction and a subclass of Optimizer
RMSProp(learning_rate[, beta, jitter])	RMSprop optimization method
AdaGrad(learning_rate[, jitter])	Adagrad optimization method
SASA(sgo, dim[, theta, rho, W0, t_check, …])	A class of Statistical Adaptive Stochastic Gradient Optimizer