pymc.Approximation#

class pymc.Approximation(groups, model=None)[source]#

Wrapper for grouped approximations

Wraps list of groups, creates an Approximation instance that collects sampled variables from all the groups, also collects logQ needed for explicit Variational Inference.

Parameters:

groups: list[Group]: List of Group instances. They should have all model variables
model: Model

See also

Group

Notes

Some shortcuts for single group approximations are available:

MeanField

FullRank

Empirical

Methods

`Approximation.__init__`(groups[, model])
`Approximation.collect`(item)
`Approximation.get_optimization_replacements`(s, d)	Dev - optimizations for logP.
`Approximation.make_size_and_deterministic_replacements`(s, d)	Dev - creates correct replacements for initial depending on sample size and deterministic flag
`Approximation.rslice`(name)	Dev - vectorized sampling for named random variable without call to pytensor.scan.
`Approximation.sample`([draws, random_seed, ...])	Draw samples from variational posterior.
`Approximation.sample_node`(node[, size, ...])	Samples given node or nodes over shared posterior
`Approximation.set_size_and_deterministic`(...)	Dev - after node is sampled via `symbolic_sample_over_posterior()` or `symbolic_single_sample()` new random generator can be allocated and applied to node
`Approximation.symbolic_sample_over_posterior`(node)	Dev - performs sampling of node applying independent samples from posterior each time.
`Approximation.symbolic_single_sample`(node[, ...])	Dev - performs sampling of node applying single sample from posterior.
`Approximation.to_flat_input`(node[, ...])	Dev - replace vars with flattened view stored in self.inputs

Attributes

`all_histograms`
`any_histograms`
`datalogp`	Dev - computes \(E_{q}(data term)\) from model via pytensor.scan that can be optimized later
`datalogp_norm`	Dev - normalized \(E_{q}(data term)\)
`ddim`
`has_logq`
`inputs`
`joint_histogram`
`logp`	Dev - computes \(E_{q}(logP)\) from model via pytensor.scan that can be optimized later
`logp_norm`	Dev - normalized \(E_{q}(logP)\)
`logq`	Dev - collects logQ for all groups
`logq_norm`	Dev - collects logQ for all groups and normalizes it
`ndim`
`params`
`replacements`	Dev - all replacements from groups to replace PyMC random variables with approximation
`sample_dict_fn`
`scale_cost_to_minibatch`	Dev - Property to control scaling cost to minibatch
`single_symbolic_datalogp`	Dev - for single MC sample estimate of \(E_{q}(data term)\) pytensor.scan is not needed and code can be optimized
`single_symbolic_logp`	Dev - for single MC sample estimate of \(E_{q}(logP)\) pytensor.scan is not needed and code can be optimized
`single_symbolic_varlogp`	Dev - for single MC sample estimate of \(E_{q}(prior term)\) pytensor.scan is not needed and code can be optimized
`sized_symbolic_datalogp`	Dev - computes sampled data term from model via pytensor.scan
`sized_symbolic_logp`	Dev - computes sampled logP from model via pytensor.scan
`sized_symbolic_varlogp`	Dev - computes sampled prior term from model via pytensor.scan
`symbolic_logq`	Dev - collects symbolic_logq for all groups
`symbolic_normalizing_constant`	Dev - normalizing constant for self.logq, scales it to minibatch_size instead of total_size.
`symbolic_random`
`symbolic_randoms`
`varlogp`	Dev - computes \(E_{q}(prior term)\) from model via pytensor.scan that can be optimized later
`varlogp_norm`	Dev - normalized \(E_{q}(prior term)\)