Fast Online Estimation of the Joint Probability Distribution
Fast Online Estimation of the Joint Probability Distribution
复制标题
联合概率分布的快速在线估计
DOI：
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复制
发表时间：
2008
期刊：
影响因子：
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通讯作者：
Jan Peter Patist
中科院分区：
文献类型：
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作者：
Jan Peter Patist
本文提出了一种数据流联合概率分布的在线维护算法。联合概率分布由低依赖贝叶斯网络的混合建模，并由在线EM算法维护。用低依赖贝叶斯网络的混合建模联合概率函数的动机是两个关键观察。首先，概率分布可以保持时间成本在数据点的数量中线性，并且每个数据点的时间恒定。而其他方法，如贝叶斯网络，则具有多项式时间复杂性。其次，从文献上看，有经验表明，朴素-贝叶斯结构的混合可以像贝叶斯网络一样准确地建模数据。本文将朴素-贝叶斯结构混合模型的约束放宽为任意低依赖结构混合模型的约束。此外，我们还提出了一种在线维护任意贝叶斯网络混合模型的算法。我们的经验表明，在不降低性能的情况下实现了加速。
In this paper we propose an algorithm for the on-line maintenance of the joint probability distribution of a data stream. The joint probability distribution is modeled by a mixture of low dependence Bayesian networks, and maintained by an on-line EM-algorithm. Modeling the joint probability function by a mixture of low dependence Bayesian networks is motivated by two key observations. First, the probability distribution can be maintained with time cost linear in the number of data points and constant time per data point. Whereas other methods like Bayesian networks have polynomial time complexity. Secondly, looking at the literature there is empirical indication [1] that mixtures of Naive-Bayes structures can model the data as accurate as Bayesian networks. In this paper we relax the constraints of the mixture model of Naive-Bayes structures to that of the mixture models of arbitrary low dependence structures. Furthermore we propose an on-line algorithm for the maintenance of a mixture model of arbitrary Bayesian networks. We empirically show that speed-up is achieved with no decrease in performance.
DOI：
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发表时间：
1998-11
期刊：
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影响因子：
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作者：
P. Bradley;U. Fayyad;Cory Reina
通讯作者：
P. Bradley;U. Fayyad;Cory Reina