Export Reviews, Discussions, Author Feedback and Meta-Reviews

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper is concerned with Sequential Monte Carlo Methods for Probabilistic Graphical Models (PGM). The main contribution of this paper is that it introduces a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. The authors make use of the structure of the PGM to define a sequence of intermediate target distributions for the sampler. The SMC sampler that is proposed can be then used within a Particle MCMC algorithm to come with efficient algorithms both for parameter and state estimation.