General black-box framework¶
“Black box”¶
In order to have minimal amount of model specific computations, one can use black box variational inference ([Paisley:2013][Ranganath:2014]). It uses stochastic optimization and computes noisy gradients of the VB lower bound by sampling from the approximate posterior distribution \(q(Z)\) to estimate the relevant expectations. In principle, the method can be applied to any model for which the (unnormalized) joint density \(p(Y,Z)\) can be computed.
Variational approximation as linear regression¶
[Salimans:2013]