Variational Inference

ADVI(*args, **kwargs)	Automatic Differentiation Variational Inference (ADVI)
ASVGD([approx, estimator, kernel])	Amortized Stein Variational Gradient Descent
SVGD([n_particles, jitter, model, start, ...])	Stein Variational Gradient Descent
FullRankADVI(*args, **kwargs)	Full Rank Automatic Differentiation Variational Inference (ADVI)
ImplicitGradient(approx[, estimator, kernel])	Implicit Gradient for Variational Inference
Inference(op, approx, tf, **kwargs)	Base class for Variational Inference
KLqp(approx[, beta])	Kullback Leibler Divergence Inference
fit([n, method, model, random_seed, start, ...])	Handy shortcut for using inference methods in functional way

Approximations

Empirical([trace, size])	Single Group Full Rank Approximation
FullRank(*args, **kwargs)	Single Group Full Rank Approximation
MeanField(*args, **kwargs)	Single Group Mean Field Approximation
sample_approx(approx[, draws, ...])	Draw samples from variational posterior.

OPVI

Approximation(groups[, model])	Wrapper for grouped approximations
Group([group, vfam, params])	Base class for grouping variables in VI

Operators

KL(approx[, beta])	Operator based on Kullback Leibler Divergence
KSD(approx[, temperature])	Operator based on Kernelized Stein Discrepancy

Special

Stein(approx[, kernel, use_histogram, ...])
adadelta([loss_or_grads, params, ...])	Adadelta updates
adagrad([loss_or_grads, params, ...])	Adagrad updates
adagrad_window([loss_or_grads, params, ...])	Returns a function that returns parameter updates.
adam([loss_or_grads, params, learning_rate, ...])	Adam updates
adamax([loss_or_grads, params, ...])	Adamax updates
apply_momentum(updates[, params, momentum])	Returns a modified update dictionary including momentum
apply_nesterov_momentum(updates[, params, ...])	Returns a modified update dictionary including Nesterov momentum
momentum([loss_or_grads, params, ...])	Stochastic Gradient Descent (SGD) updates with momentum
nesterov_momentum([loss_or_grads, params, ...])	Stochastic Gradient Descent (SGD) updates with Nesterov momentum
norm_constraint(tensor_var, max_norm[, ...])	Max weight norm constraints and gradient clipping
rmsprop([loss_or_grads, params, ...])	RMSProp updates
sgd([loss_or_grads, params, learning_rate])	Stochastic Gradient Descent (SGD) updates
total_norm_constraint(tensor_vars, max_norm)	Rescales a list of tensors based on their combined norm