- BinomialBayesMixedGLM.fit_vb(mean=None, sd=None, fit_method='BFGS', minim_opts=None, scale_fe=False, verbose=False)¶
Fit a model using the variational Bayes mean field approximation.
Starting value for VB mean vector
Starting value for VB standard deviation vector
Algorithm for scipy.minimize
Options passed to scipy.minimize
If true, the columns of the fixed effects design matrix are centered and scaled to unit variance before fitting the model. The results are back-transformed so that the results are presented on the original scale.
If True, print the gradient norm to the screen each time it is calculated.
The goal is to find a factored Gaussian approximation q1*q2*… to the posterior distribution, approximately minimizing the KL divergence from the factored approximation to the actual posterior. The KL divergence, or ELBO function has the form
E* log p(y, fe, vcp, vc) - E* log q
where E* is expectation with respect to the product of qj.
Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A review for Statisticians https://arxiv.org/pdf/1601.00670.pdf