Abstract:
We consider an estimation problem for a backward stochastic differential equation in the presence of statistically indeterminate noise. We use the approach of the theory of guaranteed estimation and assume that the statistically indeterminate noise, as well as some processes entering the equation, is subject to integral constraints. In the linear case, we prove a theorem on the approximation of random information sets by deterministic sets as the diffusion coefficient vanishes. Examples are considered.
Keywords:
backward stochastic differential equation, Brownian motion, random information set.
\Bibitem{Ana14}
\by B.~I.~Anan'ev
\paper On the estimation of backward stochastic differential equations
\serial Trudy Inst. Mat. i Mekh. UrO RAN
\yr 2014
\vol 20
\issue 4
\pages 17--28
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\transl
\jour Proc. Steklov Inst. Math. (Suppl.)
\yr 2016
\vol 292
\issue , suppl. 1
\pages 14--26
\crossref{https://doi.org/10.1134/S0081543816020024}
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Linking options:
https://www.mathnet.ru/eng/timm1111
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This publication is cited in the following 1 articles:
B. I. Ananyev, “Finitely Approximable Random Sets and Their Evolution Via Differential Equations”, Applications of Mathematics in Engineering and Economics (AMEE'16), AIP Conference Proceedings, 1789, eds. V. Pasheva, N. Popivanov, G. Venkov, Amer. Inst. Physics, 2016, UNSP 040012