We propose a Bayesian nonparametric approach to the problem of jointly
modeling multiple related time series. Our approach is based on the discovery
of a set of latent, shared dynamical behaviors. Using a beta process prior, the
size of the set and the sharing pattern are both inferred from data. We develop
efficient Markov chain Monte Carlo methods based on the Indian buffet process
representation of the predictive distribution of the beta process, without
relying on a truncated model. In particular, our approach uses the sum-product
algorithm to efficiently compute Metropolis-Hastings acceptance probabilities,
and explores new dynamical behaviors via birth and death proposals. We examine
the benefits of our proposed feature-based model on several synthetic datasets,
and also demonstrate promising results on unsupervised segmentation of visual
motion capture data.

该研究提出了使用贝叶斯非参数方法在多个相关时间序列的联合建模问题上的解决方案，其中使用了潜在的共享动态行为发现方法，提出了一种基于 Beta 过程先验的高效 Markov chain Monte Carlo 方法，并利用 birth 和 death proposals 方法探究新的动态行为，最终实现了对于视频运动捕捉数据的无监督分割，并在一些合成数据集中获得了良好的结果。