6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm

      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

          Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stake issue. In past years, multiple methods have been proposed from different communities to tackle this difficult sampling task ranging from iterative numerical linear algebra to Markov chain Monte Carlo (MCMC) approaches. Surprisingly, no complete review and comparison of these methods have been conducted. This paper aims at reviewing all these approaches by pointing out their differences, close relations, benefits and limitations. In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization. This framework offers a novel and unifying revisit of most of the existing MCMC approaches while extending them. Guidelines to choose the appropriate Gaussian simulation method for a given sampling problem are proposed and illustrated with numerical examples.

          Related collections

          Author and article information

          Journal
          04 October 2020
          Article
          2010.01510
          29a7063b-3a4a-46e1-b7e5-1557bf6086bd

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

          History
          Custom metadata
          47 pages
          stat.CO

          Mathematical modeling & Computation
          Mathematical modeling & Computation

          Comments

          Comment on this article