17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Approximation Algorithms for the Loop Cutset Problem

      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 show how to find a small loop curser in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset. We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms' output and the number of instances associated with a minimum solution is 1.22.

          Related collections

          Most cited references2

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

          Identifying independence in bayesian networks

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

            Probabilistic inference in multiply connected belief networks using loop cutsets

              Bookmark

              Author and article information

              Journal
              1302.6787

              Data structures & Algorithms,Artificial intelligence
              Data structures & Algorithms, Artificial intelligence

              Comments

              Comment on this article