Particle Filters for Partially Observed Diffusions
| dc.creator | Fearnhead, Paul | |
| dc.creator | Papaspiliopoulos, Omiros | |
| dc.creator | Roberts, Gareth | |
| dc.date | 2007-10-23 | |
| dc.date.accessioned | 2026-07-07T08:38:05Z | |
| dc.date.available | 2026-07-07T08:38:05Z | |
| dc.description | In this paper we introduce a novel particle filter scheme for a class of partially-observed multivariate diffusions. %continuous-time dynamic models where the %signal is given by a multivariate diffusion process. We consider 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 currently 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 the diffusion process and the unbiased estimation of the transition density as described in \cite{besk:papa:robe:fear:2006}. %In particular, w We introduce the Generalised Poisson Estimator, which generalises the Poisson Estimator of \cite{besk:papa:robe:fear:2006}. %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 theoretically by a A central limit theorem is given for our particle filter scheme. | |
| dc.identifier | https://arxiv.org/abs/0710.4245 | |
| dc.identifier | http://arxiv.org/abs/0710.4245 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/140598 | |
| dc.subject | Methodology | |
| dc.subject | Computation | |
| dc.title | Particle Filters for Partially Observed Diffusions | |
| dc.type | text |