We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sam
Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques: A Survey and Comparative Study
β Scribed by Tanizaki H.
- Year
- 2000
- Tongue
- English
- Leaves
- 64
- Category
- Library
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π SIMILAR VOLUMES
For the last decade, various simulation-based nonlinear and non-Gaussian filters and smoothers have been proposed. In the case where the unknown parameters are included in the nonlinear and non-Gaussian system, however, it is very difficult to estimate the parameters together with the state variable
The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and HΓΌrzeler and KΓΌnsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Gewek
A non-linear and/or non-normal filter is proposed in this paper. Generating random draws of the state vector directly from the filtering density, the filtering estimate is obtained, which gives us a recursive algorithm. There, we do not evaluate any integration included in the density-based filterin