Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
复制标题
对具有跨段依赖性的多个变点模型进行高效贝叶斯分析
作者：
P. Fearnhead;Z. Liu
研究了一类多变点模型的贝叶斯分析。虽然有各种有效的方法来分析这些模型，如果与每个部分相关联的参数是独立的，有几个一般的方法模型的参数是依赖的。假设依赖是马尔可夫的，我们提出了一个有效的在线算法从近似的后验分布的变化点的数量和位置进行采样。在模拟研究中，我们表明，引入的近似是可以忽略的。我们说明了我们的方法的力量，通过拟合分段多项式模型的数据，在一个模型下，允许连续性或不连续性的基础曲线在每个变化点。这种方法是有竞争力的，或优于，从噪声数据推断曲线的其他方法，唯一的，它允许推断的基础曲线中的不连续性的位置。
We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the dependence is Markov, we propose an efficient online algorithm for sampling from an approximation to the posterior distribution of the number and position of the changepoints. In a simulation study, we show that the approximation introduced is negligible. We illustrate the power of our approach through fitting piecewise polynomial models to data, under a model which allows for either continuity or discontinuity of the underlying curve at each changepoint. This method is competitive with, or outperform, other methods for inferring curves from noisy data; and uniquely it allows for inference of the locations of discontinuities in the underlying curve.