pymc.FullRankADVI#

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

Full Rank Automatic Differentiation Variational Inference (ADVI)

Parameters:
model: :class:`pymc.Model`

PyMC model for inference

random_seed: None or int
start: `dict[str, np.ndarray]` or `StartDict`

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