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Sequential Monte Carlo has become a standard tool for Bayesian Inference ofcomplex models. This approach can be computationally demanding, especially wheninitialized from the prior distribution. On the other hand, deter-ministicapproximations of the posterior distribution are often available with notheoretical guaranties. We propose a bridge sampling scheme starting from sucha deterministic approximation of the posterior distribution and targeting thetrue one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence ofdistributions that is determined in an adaptive way. We illustrate therobustness and the efficiency of the methodology on a large simulation study.When applied to network datasets, SBS inference leads to different statisticalconclusions from the one supplied by the standard variational Bayesapproximation.

Using deterministic approximations to accelerate SMC for posterior sampling