资源分类
》 资源 》论文》Testing Unfaithful Gaussian Graphical Models

Testing Unfaithful Gaussian Graphical Models

2020-01-19 | |  55 |   43 |   0

Abstract

The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set S graph separates nodes u and v then 图片.png is conditionally independent of 图片.png given 图片.png . The opposite direction need not be true, that is, 图片.png need not imply S is a node separator of u and v. When it does, the relation 图片.png is called faithful. In this paper we provide a characterization of faithful relations and then provide an algorithm to test faithfulness based only on knowledge of other conditional relations of the form 图片.png .

上一篇：An Accelerated Proximal Coordinate Gradient Method

下一篇：Large-Margin Convex Polytope Machine

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...