gaussian graphical models (GGMs) or Gauss Markov random fields are widely
used in many applications, and the trade-off between the modeling capacity and
the efficiency of learning and inference has been an important research
problem. In this paper, we study the family of GGMs with smal
研究了连接反馈顶点集(Connected Feedback Vertex Set)问题的参数化算法,论述了该问题在图论领域的应用,证明了在普通图上复杂度为 O (2^O (k) n^O (1)),在不包含固定图 H 的图上复杂度为 O (2^O (√k logk) n^O (1)),此外将 Group Steiner Tree 问题用作子程序,并认为其独立于其他算法,具有重要意义。