Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

23 May 2018 Marcel Hirt Petros Dellaportas

We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static parameters of the model... (read more)

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