We propose algorithms for approximate filtering and smoothing in high-dimensional factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according a notion of locality in a factor graph associated with the emission distribution. This allows the exponential-in-dimension cost of exact filtering and smoothing to be avoided. We prove that the approximation accuracy, measured in a local total variation norm, is `dimension-free' in the sense… CONTINUE READING

Graph Filter and Graph Smoother are algorithms for approximate filtering and smoothing in high-dimensional factorial hidden Markov models. The approximation involves discarding, in a principled way, likelihood factors according a notion of locality in a factor graph associated with the emission distribution.