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


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.


Approximation(groups[, model])

Wrapper for grouped approximations

Group([group, vfam, params])

Base class for grouping variables in VI


KL(approx[, beta])

Operator based on Kullback Leibler Divergence

KSD(approx[, temperature])

Operator based on Kernelized Stein Discrepancy


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