5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Replica Conditional Sequential Monte Carlo

      Preprint

      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this problem rely on particle MCMC techniques and its variants, such as the iterated conditional Sequential Monte Carlo (cSMC) scheme, which uses a Sequential Monte Carlo (SMC) type proposal within MCMC. A deficiency of standard SMC proposals is that they only use observations up to time \(t\) to propose states at time \(t\) when an entire observation sequence is available. More sophisticated SMC based on lookahead techniques could be used but they can be difficult to put in practice. We propose here replica cSMC where we build SMC proposals for one replica using information from the entire observation sequence by conditioning on the states of the other replicas. This approach is easily parallelizable and we demonstrate its excellent empirical performance when compared to the standard iterated cSMC scheme at fixed computational complexity.

          Related collections

          Most cited references 7

          • Record: found
          • Abstract: not found
          • Article: not found

          Particle Markov chain Monte Carlo methods

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Smoothing algorithms for state–space models

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The Iterated Auxiliary Particle Filter

                Bookmark

                Author and article information

                Journal
                13 May 2019
                Article
                1905.05255

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                To appear in Proceedings of ICML '19
                stat.CO

                Mathematical modeling & Computation

                Comments

                Comment on this article