Stochastic Gradient MCMC for Nonlinear State Space Models

Understand

State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process.

• Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series.
• The challenge is two-fold: not only do computations scale linearly with time, as in the linear case, but particle filters additionally suffer from increasing particle degeneracy with longer series.
• Stochastic gradient MCMC methods have been developed to scale Bayesian inference for finite-state hidden Markov models and linear SSMs using buffered stochastic gradient estimates to account for temporal dependencies.