Gaussian process modulated renewal processes
Gaussian process modulated renewal processes
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发表时间：
2011-12
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通讯作者：
Vinayak A. Rao;Y. Teh
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作者：
Vinayak A. Rao;Y. Teh
更新过程是Poisson过程在真实的直线上的推广，直线的间隔是独立同分布的.从一些分布。调制更新过程允许这些intervent分布随时间变化，允许引入非平稳性。在这项工作中，我们采取了非参数贝叶斯方法，这种非平稳性与高斯过程建模。我们的方法是基于均匀化的思想，这使我们能够从一个难以处理的分布中提取精确的样本。我们开发了一种新的和有效的MCMC采样后验推理。在我们的实验中，我们测试了一些合成和真实的数据集。
Renewal processes are generalizations of the Poisson process on the real line whose intervals are drawn i.i.d. from some distribution. Modulated renewal processes allow these interevent distributions to vary with time, allowing the introduction of nonstationarity. In this work, we take a nonparametric Bayesian approach, modelling this nonstationarity with a Gaussian process. Our approach is based on the idea of uniformization, which allows us to draw exact samples from an otherwise intractable distribution. We develop a novel and efficient MCMC sampler for posterior inference. In our experiments, we test these on a number of synthetic and real datasets.