  Probabilistic programming languages can simplify the development of machine
learning techniques, but only if inference is sufficiently scalable.
Unfortunately, Bayesian parameter estimation for highly coupled models such as
regressions and state-space models still scales poorly; each MCMC transition
takes linear time in the number of observations. This paper describes a
sublinear-time algorithm for making Metropolis-Hastings (MH) updates to latent
variables in probabilistic programs. The approach generalizes recently
introduced approximate MH techniques: instead of subsampling data items assumed
to be independent, it subsamples edges in a dynamically constructed graphical
model. It thus applies to a broader class of problems and interoperates with
other general-purpose inference techniques. Empirical results, including
confirmation of sublinear per-transition scaling, are presented for Bayesian
logistic regression, nonlinear classification via joint Dirichlet process
mixtures, and parameter estimation for stochastic volatility models (with state
estimation via particle MCMC). All three applications use the same
implementation, and each requires under 20 lines of probabilistic code.

Machine Learning