Marginalized particle Gibbs for multiple state-space models coupled through shared parameters
通过共享参数耦合的多个状态空间模型的边缘化粒子 Gibbs 方法
We consider Bayesian inference from multiple time series described by a common state-space model (SSM) structure, but where different subsets of parameters are shared between different submodels. An important example is disease-dynamics, where parameters can be either disease or location specific. Parameter inference in these models can be improved by systematically aggregating information from the different time series, most notably for short series. Particle Gibbs (PG)…