Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. 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. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods. While generally applicable, we show explicitly how this can be done with loopy belief propagation, expectation propagation, and Laplace approximations. The resulting algorithm can be viewed as a post-correction of the biases associated with these methods and, indeed, numerical results show clear improvements over the baseline deterministic methods as well as over plain SMC.

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.

Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods. The former can often provide accurate and rapid inferences, but are typically associated with biases that are hard to quantify. 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. Specifically, we suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which can leverage the output from deterministic inference methods.