Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Fredrik Lindsten, Jouni Helske, Matti Vihola

Neural Information Processing Systems 

The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference.

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