Stochastic Gradient MCMC for State Space Models

Understand

State space models (SSMs) are a flexible approach to modeling complex time series.

• However, inference in SSMs is often computationally prohibitive for long time series.
• Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data.
• Unfortunately when applied to dependent data, such as in SSMs, SGMCMC's stochastic gradient estimates are biased as they break crucial temporal dependencies.