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Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques: A Survey and Comparative Study

✍ Scribed by Tanizaki H.


Year
2000
Tongue
English
Leaves
64
Category
Library

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πŸ“œ SIMILAR VOLUMES


Nonlinear and non-Gaussian state-space m
✍ Tanizaki H., Mariano R. S. πŸ“‚ Library πŸ“… 1998 🌐 English

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

Estimation of unknown parameters in nonl
✍ Tanizaki H. πŸ“‚ Library πŸ“… 2000 🌐 English

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

onlinear and non-Gaussian state estimati
✍ Hisashi Tanizaki πŸ“‚ Library πŸ“… 1998 🌐 English

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

Non-linear and non-normal filter based o
✍ Hisashi Tanizaki πŸ“‚ Library πŸ“… 1997 🌐 English

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