资源分类
》 资源 》论文》Graphical Models for Inference with Missing Data

Graphical Models for Inference with Missing Data

2020-01-16 | |  51 |   36 |   0

Abstract

We address the problem of recoverability i.e. deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called ‘Missingness Graphs’ to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this representation, we derive conditions that the graph should satisfy to ensure recoverability and devise algorithms to detect the presence of these conditions in the graph.

上一篇：Scalable kernels for graphs with continuous attributes

下一篇：Multi-Prediction Deep Boltzmann Machines

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

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

  • Rating-Boosted La...

    The performance of a recommendation system reli...