Title: Using deterministic approximations to accelerate SMC for posterior sampling,
Abstract: Sequential Monte Carlo has become a standard tool for Bayesian Inference of
complex models. This approach can be computationally demanding, especially when
initialized from the prior distribution. On the other hand, deter-ministic
approximations of the posterior distribution are often available with no
theoretical guaranties. We propose a bridge sampling scheme starting from such
a deterministic approximation of the posterior distribution and targeting the
true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of
distributions that is determined in an adaptive way. We illustrate the
robustness and the efficiency of the methodology on a large simulation study.
When applied to network datasets, SBS inference leads to different statistical
conclusions from the one supplied by the standard variational Bayes
approximation.