Stochastic Gradient MCMC for Nonlinear State Space Models
arXiv: Machine Learning, 2019.
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 lin...More
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