https://studiegids.vu.nl/en/courses/2024-2025/XB_0107At the end of this course students will ... ... be acquainted with the dominant concepts of data assimilation methods, including some theoretical background; ... acquire knowledge of established data assimilation algorithms such as Gaussian methods, Markov Chain-Monte Carlo Methods and variational methods; ... be able to apply data assimilation methods in a scientific setting; ... be able to evaluate the reliability of data assimilation methods; ... be able to report comprehensively on the structure of their algorithms as well as the computations performed using their code.Dynamical systems come across in many fields: for example climatology, biology, and engineering. If the initial condition of a dynamical system is uncertain, then probabilistic forecasting is used to provide a set of plausible predictions with probability assigned to it. The aim of the course is to describe the mathematical and algorithmic principles of probabilistic forecasting of dynamical systems.Exercise classes and computer classes. Contact hours per week: 8.The final grade is based on a set of reports and computer codes that have to be handed in. The number and weights of the assignments will be specified on Canvas at the start of the course. Resit opportunities: if the weighted average of the submitted assignments fails to surpass the necessary 55% needed to pass the course, then there will be an option of re-submitting inadequate submissions. A re-submission cannot attain a score greater than 60%.K .J. H. Law, A. M. Stuart and K. C. Zygalakis, Data Assimilation: A Mathematical Introduction. Springer, 242pp.BSc Mathematics Year 2Additional information on CanvasLinear Algebra, Probability Theory, Mathematical Statistics, Numerical Methods, Dynamical Systems