pymc.FullRankADVI#

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

Full Rank Automatic Differentiation Variational Inference (ADVI)

Parameters
local_rv: dict[var->tuple]

mapping {model_variable -> approx params} Local Vars are used for Autoencoding Variational Bayes See (AEVB; Kingma and Welling, 2014) for details

model:class:pymc.Model

PyMC model for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

start: `Point`

starting point for inference

References

  • Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2016). Automatic Differentiation Variational Inference. arXiv preprint arXiv:1603.00788.

  • Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI approximateinference.org/accepted/RoederEtAl2016.pdf

  • Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. stat, 1050, 1.

Methods

FullRankADVI.__init__(*args, **kwargs)

FullRankADVI.fit([n, score, callbacks, ...])

Perform Operator Variational Inference

FullRankADVI.refine(n[, progressbar])

Refine the solution using the last compiled step function

FullRankADVI.run_profiling([n, score])

Attributes

approx