Sequential Monte Carlo for Graphical Models
This work addresses inference challenges in probabilistic graphical models, offering a novel method for researchers and practitioners in machine learning and statistics, though it appears incremental as it builds on existing SMC and MCMC techniques.
The paper tackles the problem of inference in probabilistic graphical models by proposing a sequential Monte Carlo framework that decomposes the model into auxiliary distributions, resulting in an unbiased estimate of the partition function and enabling high-dimensional block-sampling algorithms.
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.