Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
نویسندگان
چکیده
We 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. We propose a linear regression model within a 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’ evolution. The resulting hybrid filter locally optimizes the weights to achieve the best fit of a nonlinear signal with a single linear model.
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ورودعنوان ژورنال:
- Communications in Statistics - Simulation and Computation
دوره 41 شماره
صفحات -
تاریخ انتشار 2012