Testing Bayesian Networks
Testing Bayesian Networks
复制标题
测试贝叶斯网络
DOI：
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复制
发表时间：
2016
影响因子：
2.5
通讯作者：
Alistair Stewart
中科院分区：
文献类型：
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作者：
C. Canonne;Ilias Diakonikolas;D. Kane;Alistair Stewart
这项工作启动了测试高维结构化分布的系统投资，该分布专注于贝叶斯网络 - 有向图形模型的原型家族任何特定的节点在所有其他非脱位节点上都独立于其父母的固定，我们研究了贝叶斯网络的身份测试和紧密的测试。对于这些问题和相应的信息理论下限。
This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks – the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node. The value at any particular node is conditionally independent of all the other non-descendant nodes once its parents are fixed. Specifically, we study the properties of identity testing and closeness testing of Bayesian networks. Our main contribution is the first non-trivial efficient testing algorithms for these problems and corresponding information-theoretic lower bounds. For a wide range of parameter settings, our testing algorithms have sample complexity sublinear in the dimension and are sample-optimal, up to constant factors.
DOI：
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发表时间：
2016-06
期刊：
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影响因子：
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作者：
Yu Cheng;Ilias Diakonikolas;D. Kane;Alistair Stewart
通讯作者：
Yu Cheng;Ilias Diakonikolas;D. Kane;Alistair Stewart
近线性时间内的样本最优密度估计
DOI：
10.48550/arxiv.1506.00671
发表时间：
2015
期刊：
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影响因子：
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作者：
Acharya J
通讯作者：
Acharya J