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).
fit([n, method, model, random_seed, start, ...])	Handy shortcut for using inference methods in functional way.

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.

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

Group([group, vfam, params])

Base class for grouping variables in VI.

Approximation(groups[, model])

Wrapper for grouped approximations.

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, ...])

Streaming

Out-of-core minibatching for variational inference on datasets that do not fit in memory (see pymc.variational.streaming).

DataLoader(dataset, *, batch_size[, ...])	Turn an out-of-core dataset into fixed-size minibatches for variational inference.
IterableDataset()	A re-iterable, out-of-core source of rows, like torch.utils.data.IterableDataset.
Trainer(*[, method, model, data_name])	Drive variational inference over a DataLoader without user callbacks.
shuffle_buffer(chunk_source, *, buffer_size, ...)	Wrap a chunk source into a shuffled, fixed-size batch source.
parquet_source(directory, *[, columns, pattern])	An IterableDataset over a directory of Parquet files.

adadelta([loss_or_grads, params, ...])	Adadelta updates.
adagrad([loss_or_grads, params, ...])	Adagrad updates.
adagrad_window([loss_or_grads, params, ...])	Return 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])	Return a modified update dictionary including momentum.
apply_nesterov_momentum(updates[, params, ...])	Return 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.