Efficient Bayesian Inference for Dynamic Mixture Models
提出一种贝叶斯方法估计线性高斯状态空间模型的混合，通过推导递归公式实现高效马尔可夫链蒙特卡洛采样，快速收敛到后验分布，并用污泥锌浓度数据演示。
Abstract A Bayesian approach is presented for estimating a mixture of linear Gaussian state-space models. Such models are used to model interventions in time series and nonparametric regression. Markov chain Monte Carlo sampling is usually necessary to obtain the posterior distributions of such mixture models, because it is difficult to obtain them analytically. The methodological contribution of the article is to derive a set of recursions for dynamic mixture models that efficiently implement a Markov chain Monte Carlo sampling scheme that converges rapidly to the posterior distribution. The methodology is illustrated by fitting an autoregressive model subject to interventions to zinc concentration in sludge.