Divide-and-Conquer with Sequential Monte Carlo

19 Jun 2014Fredrik LindstenAdam M. JohansenChristian A. NaessethBonnie KirkpatrickThomas B. SchönJohn AstonAlexandre Bouchard-Côté

We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree-structured decomposition of the model of interest, turning the overall inferential task into a collection of recursively solved sub-problems... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.