An optimal Gauss-Markov approximation for a process with stochastic drift and applications

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F20%3A00534260" target="_blank" >RIV/67985823:_____/20:00534260 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.spa.2020.05.018" target="_blank" >https://doi.org/10.1016/j.spa.2020.05.018</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.spa.2020.05.018" target="_blank" >10.1016/j.spa.2020.05.018</a>

Result language
angličtina
Original language name
An optimal Gauss-Markov approximation for a process with stochastic drift and applications
Original language description
We consider a linear stochastic differential equation with stochastic drift. We study the problem of approximating the solution of such equation through an Ornstein–Uhlenbeck type process, by using direct methods of calculus of variations. We show that general power cost functionals satisfy the conditions for existence and uniqueness of the approximation. We provide some examples of general interest and we give bounds on the goodness of the corresponding approximations. Finally, we focus on a model of a neuron embedded in a simple network and we study the approximation of its activity, by exploiting the aforementioned results.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GA20-10251S" target="_blank" >GA20-10251S: Optimality of neuronal communication: an information-theoretic perspective</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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