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Optimal Control of Probability on A Target Set for Continuous-Time Markov Chains (2023)
Journal Article
Ma, C., & Zhao, H. (online). Optimal Control of Probability on A Target Set for Continuous-Time Markov Chains. IEEE Transactions on Automatic Control, 69(2), 1202-1209. https://doi.org/10.1109/tac.2023.3278789

In this paper, a stochastic optimal control problem is considered for a continuous-time Markov chain taking values in a denumerable state space over a fixed finite horizon. The optimality criterion is the probability that the process remains in a tar... Read More about Optimal Control of Probability on A Target Set for Continuous-Time Markov Chains.

Existence of geometric ergodic periodic measures of stochastic differential equations (2023)
Journal Article
Feng, C., Zhao, H., & Zhong, J. (2023). Existence of geometric ergodic periodic measures of stochastic differential equations. Journal of Differential Equations, 359, 67-106. https://doi.org/10.1016/j.jde.2023.02.022

Periodic measures are the time-periodic counterpart to invariant measures for dynamical systems and can be used to characterise the long-term periodic behaviour of stochastic systems. This paper gives sufficient conditions for the existence, uniquene... Read More about Existence of geometric ergodic periodic measures of stochastic differential equations.

Periodic measures and Wasserstein distance for analysing periodicity of time series datasets (2023)
Journal Article
Feng, C., Liu, Y., & Zhao, H. (2023). Periodic measures and Wasserstein distance for analysing periodicity of time series datasets. Communications in Nonlinear Science and Numerical Simulation, 120, Article 107166. https://doi.org/10.1016/j.cnsns.2023.107166

In this article, we establish the probability foundation of the periodic measure approach in analysing periodicity of a dataset. It is based on recent work of random periodic processes. While random periodic paths provide a pathwise model for time se... Read More about Periodic measures and Wasserstein distance for analysing periodicity of time series datasets.