pymc.FullRank#

class pymc.FullRank(*args, **kwargs)[source]#

Single Group Full Rank Approximation

Full Rank approximation to the posterior where Multivariate Gaussian family is fitted to minimize KL divergence from True posterior. In contrast to MeanField approach correlations between variables are taken in account. The main drawback of the method is computational cost.

Methods

`FullRank.__init__`(args, *kwargs)
`FullRank.collect`(item)
`FullRank.get_optimization_replacements`(s, d)	Dev - optimizations for logP.
`FullRank.make_size_and_deterministic_replacements`(s, d)	Dev - creates correct replacements for initial depending on sample size and deterministic flag
`FullRank.rslice`(name)	Dev - vectorized sampling for named random variable without call to pytensor.scan.
`FullRank.sample`([draws, random_seed, ...])	Draw samples from variational posterior.
`FullRank.sample_node`(node[, size, ...])	Samples given node or nodes over shared posterior
`FullRank.set_size_and_deterministic`(node, s, d)	Dev - after node is sampled via `symbolic_sample_over_posterior()` or `symbolic_single_sample()` new random generator can be allocated and applied to node
`FullRank.symbolic_sample_over_posterior`(node)	Dev - performs sampling of node applying independent samples from posterior each time.
`FullRank.symbolic_single_sample`(node[, ...])	Dev - performs sampling of node applying single sample from posterior.
`FullRank.to_flat_input`(node[, more_replacements])	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 \(E_{q}(prior term)\)