# Nonlinear filtering for Markov systems with delayed observations

Antonella Calzolari; Patrick Florchinger; Giovanna Nappo

International Journal of Applied Mathematics and Computer Science (2009)

- Volume: 19, Issue: 1, page 49-57
- ISSN: 1641-876X

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topAntonella Calzolari, Patrick Florchinger, and Giovanna Nappo. "Nonlinear filtering for Markov systems with delayed observations." International Journal of Applied Mathematics and Computer Science 19.1 (2009): 49-57. <http://eudml.org/doc/207920>.

@article{AntonellaCalzolari2009,

abstract = {This 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,Ŷ), in the sense that Yt = Ŷa(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.},

author = {Antonella Calzolari, Patrick Florchinger, Giovanna Nappo},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {nonlinear filtering; jump processes; diffusion processes; Markov processes; stochastic delay differential equation},

language = {eng},

number = {1},

pages = {49-57},

title = {Nonlinear filtering for Markov systems with delayed observations},

url = {http://eudml.org/doc/207920},

volume = {19},

year = {2009},

}

TY - JOUR

AU - Antonella Calzolari

AU - Patrick Florchinger

AU - Giovanna Nappo

TI - Nonlinear filtering for Markov systems with delayed observations

JO - International Journal of Applied Mathematics and Computer Science

PY - 2009

VL - 19

IS - 1

SP - 49

EP - 57

AB - This 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,Ŷ), in the sense that Yt = Ŷa(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.

LA - eng

KW - nonlinear filtering; jump processes; diffusion processes; Markov processes; stochastic delay differential equation

UR - http://eudml.org/doc/207920

ER -

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