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EEA-EV - Course with Varying Content, Applied Stochastic Differential Equations, 29.10.2018-15.12.2018
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lecture 4 par...
Syllabus
Lecture 4 part 2: Itô-Taylor series of SDEs
◄ Lecture 4 part 1: Introduction, Gaussian approximations of non-linear SDEs
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ODE Basics Self-Study Task (DL 30.10.2018)
ODE Basics Quiz
Lecture 1 Part 1: Stochastic differential equations
Lecture 1 Part 2: Stochastic processes in physics and engineering
Lecture 1 Part 3: Heuristic solutions of linear SDEs
Lecture 1 Part 4: Heuristic solutions of non-linear SDEs, and Summary
Lecture 1 Quiz
Lecture 2 Part 1: Stochastic integral of Itô
Lecture 2 Part 2: Itô formula
Lecture 2 Part 3: Solutions of linear and non-linear SDEs, Summary
Lecture 2 Quiz
Lecture 3 Part 1: Fokker-Planck-Kolmogorov Equation
Lecture 3 Part 2: Moments of SDEs
Lecture 3 Part 3: Statistics of linear SDEs, Markov, Markov Properties and Transition Densities SDEs, Summary
Lecture 3 Quiz
Lecture 4 part 1: Introduction, Gaussian approximations of non-linear SDEs
Lecture 4 part 3: Itô-Taylor series based numerical methods, Summary
Lecture 4 Quiz
Lecture 5 part 1: Introduction, Runge–Kutta methods for ODEs
Lecture 5 part 2: Strong stochastic Runge–Kutta methods
Lecture 5 part 3: Weak stochastic Runge–Kutta methods, Summary
Lecture 5 Quiz
Lecture 6: Bayesian Inference in SDE Models - Problem Formulation
Lecture 6: Discrete-time Bayesian filtering and Smoothing
Lecture 6: Continuous/Discrete-Time and Continuous Bayesian Filtering and Smoothing
Lecture 6 Quiz
Exercise 1 return
Exercise 2 return
Exercise 3 return
Exercise 4 return
Exercise 5 return
Exercise 6 return
Project Work Topic Selection
Project work report return
Lecture 4 part 3: Itô-Taylor series based numerical methods, Summary ►
EEA-EV - Course with Varying Content, Applied Stochastic Differential Equations, 29.10.2018-15.12.2018
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