Data-driven inference for stochastic dynamics

Education facts

Code: MAAD35

Module 1: Stochastic Dynamics <br> 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 <br> 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 <br> 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.

Entry requirements

90 ECTS credits in Mathematics, including 30 ECTS credits at the G2F level, and upper secondary level English 6, or equivalent

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Data-driven inference for stochastic dynamics

  Karlstad University

  Karlstad

  Start date: 18 January 2027

  End date: 6 June 2027

  Pace of study: 25 %