1,720,967 research outputs found
Nonlinear Filtering for Markov Systems with Delayed Observations
No full textThis paper deals with nonlinear filtering problems with delays, i.e., we consider a system (X, Y), which can be represented by means of a system (X, (Y) over cap), in the sense that Y-t = (Y) over cap (a)(t), where a(t) is a delayed time transformation. We start with X being a Markov process, and then study Markovian systems, not necessarily diffusive, with correlated noises. The interest is focused on the existence of explicit representations of the corresponding filters as functionals depending on the observed trajectory. Various assumptions on the function a(t) are considered
Approximation of nonlinear filters for Markov systems with delayed observations
No full textWe obtain some approximation results for a class of nonlinear filtering problems with delay in the observation, i.e. systems (X, Y), which can be represented by means of a Markov system (X, (Y) over cap), in the sense that Y(t) = (Y) over cap (a)(t). To this aim we give some general upper bounds which are computed explicitly in the particular case of Markov jump processes with counting observations
Nonlinear filtering for Markov diffusion systems with delayed observations
No full textIn this paper, we consider a nonlinear filtering problem when the state process is a diffusion X/sub t/ and the observations start at a fixed time T and from that time on depend on the delayed process X/sub t-T/
Nonlinear filtering for Markov systems with delayed observations
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http://www.mat.uniroma1.it/people/nappo/papers.pdf/CFN-JAMCS-DelayMarkov.pd
Nonlinear filtering for Markov diffusion systems with delayed observations
No full textIn this paper, we consider a nonlinear filtering problem when the state process is a diffusion X-t and the observations start at a fixed time tau and from that time on depend on the delayed process Xt-tau
Approximation of nonlinear filters for Markov systems with delayed observations
No full textThe aim of this paper is to give some approximation results for a class of nonlinear filtering problems with delay in the observation. First, we point out some general results on the approximation problem for the filter in nonlinear filtering. In particular, we give a general procedure for obtaining some upper bounds for the different approximations we consider. This procedure is then applied in the case of nonlinear filtering problems with delay , which can be represented by means of a Markov system , in the sense that . Finally, these upper bounds are computed explicitly in the particular case of Markov jump process with counting observations
Convergence in nonlinear filtering for stochastic delay systems
No full textWe study an approximation scheme for a nonlinear filtering problem when the state process X is the solution of a stochastic delay diffusion equation and the observation process is a noisy function of X(s) for s is an element of [t - tau, t], where tau is a constant. The approximating state is the piecewise linear Euler-Maruyama scheme, and the observation process is a noisy function of the approximating state. The rate of convergence of this scheme is computed
Nonlinear filtering for stochastic systems with fixed delay: Approximation by a modified Milstein scheme
No full textIn this paper we study approximation schemes for a nonlinear filtering problem of a partially observed diffusive system when the state process X is the solution of a stochastic delay diffusion equation with a constant time lag tau and the observation process is a noisy function of the state. The approximating state is the linear interpolation of a modified Milstein scheme, which is asymptotically optimal with respect to the mean square l(2)-error within the class of all pathwise approximations based on equidistant observations of the driving Brownian motion. Upper bounds for the error of the filter approximations are computed. Some other discretization schemes for the state process are also considered. (C) 2011 Elsevier Ltd. All rights reserved
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