Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter

作者：Dedecius Kamil^*; Hofman Radek

来源：Communications in Statistics - Simulation and Computation, 2012, 41(5): 582-589.

DOI：10.1080/03610918.2011.598992

• Bayesian methods
• Particle filters
• Recursive estimation

The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects a priori given hypotheses on different scenarios of (expected) parameters%26apos; evolution. The resulting hybrid filter locally optimizes the weights to achieve the best fit of a nonlinear signal with a single linear model.

• 出版日期2012