Black-box variational inference (Ranganath, Gerrish & Blei 2014): a mean-field q learned by score-function ascent on the ELBO, each iteration importance sampling from q on savepoint SMC.
Black-box variational inference (Ranganath, Gerrish & Blei 2014): a mean-field q learned by score-function ascent on the ELBO, each iteration importance sampling from q on savepoint SMC.
(bbvi-infer model-task num-particles num-iterations & [opts])Black Box Variational Inference (Ranganath et al., AISTATS 2014).
Learns a mean-field q(z) = Π_addr q_addr by stochastic ascent on the ELBO
with the score-function estimator and control variates: num-iterations
updates, each from num-particles programs that sample latents from q
and weight by p(x, y)/q(x). Each site's q starts as the prior it has when
first reached and from then on moves by gradient only: mean-field, it
does not follow a prior that depends on other latents.
Args: model-task, num-particles, num-iterations opts: :base-lr (1.0) — step size at iteration t is base-lr / (t+1)^robbins-monro :robbins-monro (0.0) :adagrad (true) — true: AdaGrad, γ ∈ (0,1): RMSprop with decay γ, false: plain steps :executor
Returns: Spin<EmpiricalMeasure> — num-particles importance-weighted
samples from the final q (with 0 iterations: from the priors); the learned
q is under :variational-dists (see get-variational-dists).
Black Box Variational Inference (Ranganath et al., AISTATS 2014).
Learns a mean-field q(z) = Π_addr q_addr by stochastic ascent on the ELBO
with the score-function estimator and control variates: `num-iterations`
updates, each from `num-particles` programs that sample latents from q
and weight by p(x, y)/q(x). Each site's q starts as the prior it has when
first reached and from then on moves by gradient only: mean-field, it
does not follow a prior that depends on other latents.
Args:
model-task, num-particles, num-iterations
opts: :base-lr (1.0) — step size at iteration t is
base-lr / (t+1)^robbins-monro
:robbins-monro (0.0)
:adagrad (true) — true: AdaGrad, γ ∈ (0,1): RMSprop with decay γ,
false: plain steps
:executor
Returns: Spin<EmpiricalMeasure> — `num-particles` importance-weighted
samples from the final q (with 0 iterations: from the priors); the learned
q is under `:variational-dists` (see `get-variational-dists`).(get-variational-dists measure)The learned {address -> distribution} of a bbvi-infer result.
The learned {address -> distribution} of a `bbvi-infer` result.
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