Reviews: Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Summary and Assessment: ------------------------ This paper strives to improve Sequential Monte Carlo (SMC) sampling on probabilistic graphical models through the usage of twisted targets. More specifically, rather employ a "myopic" sequence of target distributions consisting of gradually introducing the factors and variables in the overall target (according to some ordering criteria) a method is devised by which the future can be conditionally approximated and taken into account. The idea is to devise a target that more closely approximates the true marginal distribution (\pi( x_1,...x_t) of \pi at step t rather than that resulting from dropping all future interactions. Proposition 1 presents the ideal but infeasible choice of twisting function. In effect, equation (6) defines a conditional partition function, and so approximating it with a deterministic method seems sensible. The authors present loopy BP, EP, and Laplace approximation approaches to achieve this.