Sequential tracking of an unobservable two-state Markov process under Brownian noise

08/03/2019 ∙ by Alexey Muravlev, et al. ∙ 0 ∙ share

We consider an optimal control problem, where a Brownian motion with drift is sequentially observed, and the sign of the drift coefficient changes at jump times of a symmetric two-state Markov process. The Markov process itself is not observable, and the problem consist in finding a -1,1-valued process that tracks the unobservable process as close as possible. We present an explicit construction of such a process.

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Sequential tracking of an unobservable two-state Markov process under Brownian noise

Authors

A Bayesian sequential test for the drift of a fractional Brownian motion

A sequential test for the drift of a Brownian motion with a possibility to change a decision

Contrast function estimation for the drift parameter of ergodic jump diffusion process

Intuitive Analyses via Drift Theory

Sharp large deviations for the drift parameter of the explosive Cox-Ingersoll-Ross process

Multi-Valued Routing Tracks for FPGAs in 28nm FDSOI Technology

Auto-encoders for Track Reconstruction in Drift Chambers for CLAS12

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