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In the present course stochastic linear and bilinear systems with continuous time and continuous state space are
studied. The course is focused on three topics: a) optimal control b) filtering (problem of incomplete observation)
c) problems of inference (parameter estimation).
Last update: Zichová Jitka, RNDr., Dr. (24.04.2019)
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The goal of the course is to present some basic achievements of the stochastic control and filtering theory and related topics for linear and bilinear multidimensional systems with continuous time and continuous state space Last update: T_KPMS (16.05.2013)
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The conditions for obtaining credit for the course are positive result in the exam. The credit for exercise class must be obtained prior to taking the exam.
The credit for exercise class is obtained for submission of solutions to two homework assignments in sufficient quality within specified deadlines. Last update: Maslowski Bohdan, prof. RNDr., DrSc. (25.09.2020)
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[1] B. Oksendal: Stochastic Differential Equations, 1st ed., Springer-Verlag, 1985. [2] W. H. Fleming and R. W. Rishel: Deterministic and Stochastic Optimal Control, Springer-Verlag, 1975. [3] J. Yong and X. Y. Zhou: Stochastic Controls, Hamiltonian Systems and HJB Equations, Springer-Verlag, 1999. Last update: Čoupek Petr, RNDr., Ph.D. (27.10.2019)
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Lectures and exercises are conducted in presence form. Last update: Maslowski Bohdan, prof. RNDr., DrSc. (28.09.2023)
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Exam Requirements
(may be slightly modified each year according to stuff talked over)
The exam is oral.
1. Control Theory: Dynamic programming method (i.e. optimal control obtained via the Riccati equation).
2. Filtering: Precise statement of Kalman-Bucy filter, application in Examples (as those discussed during the course).
3. Parameter estimation: Heuristic derivation by the least squares and maximum likelihood methods, strong consistency and asymptotic normality, statements of SLLN and CLT for martingales. To have an idea how to verify the conditions of these theorems in specific situations (via ergodicity). Last update: Maslowski Bohdan, prof. RNDr., DrSc. (25.09.2020)
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1. LQ problem for linear and bilinear stochastic equations in a vector space 2. The linear filtering problem, Kalman - Bucy filter 3. Some methods of parameter estimation for linear stochastic systems, properties of estimators Last update: T_KPMS (16.05.2013)
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In order to enroll in this course students should possess some basic knowledge of stochastic calculus (definition and basic properties of stochastic Ito integral, Ito formula). No preliminary knowledge of stochastic differential equations is needed. Last update: Maslowski Bohdan, prof. RNDr., DrSc. (24.05.2018)
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