pymc.KLqp#

class pymc.KLqp(approx, beta=1.0)[source]#

Kullback Leibler Divergence Inference

General approach to fit Approximations that define \(logq\) by maximizing ELBO (Evidence Lower Bound). In some cases rescaling the regularization term KL may be beneficial

\[ELBO_\beta = \log p(D|\theta) - \beta KL(q||p)\]

Parameters:

approx: :class:`Approximation`: Approximation to fit, it is required to have logQ
beta: float: Scales the regularization term in ELBO (see Christopher P. Burgess et al., 2017)

References

Christopher P. Burgess et al. (NIPS, 2017) Understanding disentangling in \(\beta\)-VAE arXiv preprint 1804.03599

Methods

`KLqp.__init__`(approx[, beta])
`KLqp.fit`([n, score, callbacks, progressbar, ...])	Perform Operator Variational Inference
`KLqp.refine`(n[, progressbar, progressbar_theme])	Refine the solution using the last compiled step function
`KLqp.run_profiling`([n, score])

Attributes

approx