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Module 1: Stochastic Dynamics
The module treats the basic theory and practice of stochastic dynamics. Theoretical key concepts include: stochastic integral, Itô formula, stochastic differential equations (SDE), existence and uniqueness of strong solutions, martingales, Markov property. Practical implementation (in R or Python) of Euler-Maruyama and Milstein methods for numerical solution of SDEs.

Module 2: Estimation for SDEs and Markov Chains
The module covers the theory of maximum likelihood and quasi-maximum likelihood estimations as well as Bayesian inference. Practical implementation (in R or Python) of Markov Chain Monte Carlo methods and the Metropolis-Hastings algorithm and its variants.

Module 3: Bayesian Filtering
The module treats the theory and practice of filtering. Theoretical key concepts include Kalman filters, extended Kalman filters, particle filters, and nonlinear filtering. Practical implementation (in R or Python) of filters with various datasets and models.

Progressive specialisation: A1N (has only first‐cycle course/s as entry requirements)

Education level: Master's level

Selection:

Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.