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- W2541795538 abstract "In this short communication we present our recent work on the construction of novel particle filters for a class of partially-observed continuous-time dynamic models where the signal is given by a multivariate diffusion process; details are deferred to [1]. Our approach directly covers a variety of observation schemes, including diffusion observed with error, observation of a subset of the components of the multivariate diffusion and arrival times of a Poisson process whose intensity is a known function of the diffusion (Cox process). Unlike available methods, our particle filters do not require approximations of the transition and/or the observation density using time-discretisations. Instead, they build on recent methodology for the exact simulation of diffusion process and the unbiased estimation of the transition density as described in the recent article [2]. In particular, we require the Generalised Poisson Estimator, which is developed in [1]. Thus, our filters avoid the systematic biases caused by time-discretisations and they have significant computational advantages over alternative continuous-time filters. These advantages are supported by a central limit theorem." @default.
- W2541795538 created "2016-11-04" @default.
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- W2541795538 date "2006-09-01" @default.
- W2541795538 modified "2023-09-23" @default.
- W2541795538 title "Particle Filtering for Diffusions Avoiding Time-Discretisations" @default.
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- W2541795538 doi "https://doi.org/10.1109/nsspw.2006.4378839" @default.
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