Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models
Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Secon...
| Үндсэн зохиолчид: | , , |
|---|---|
| Формат: | Conference item |
| Хэвлэсэн: |
2001
|
| Тойм: | Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Second, we derive simple recursions to compute posterior Cramér-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given. |
|---|