On Mixtures of Markov Chains

被引：0
|
作者：
Gupta, Rishi [1 ,2 ]
Kumar, Ravi [2 ]
Vassilvitskii, Sergei [3 ]
机构：
[1] Stanford Univ, Stanford, CA 94305 USA
[2] Google Res, Mountain View, CA USA
[3] Google Res, New York, NY 10011 USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | 2016年 / 29卷
关键词：
MAXIMUM-LIKELIHOOD;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We study the problem of reconstructing a mixture of Markov chains from the trajectories generated by random walks through the state space. Under mild non-degeneracy conditions, we show that we can uniquely reconstruct the underlying chains by only considering trajectories of length three, which represent triples of states. Our algorithm is spectral in nature, and is easy to implement.
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页数：9
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