In this series of lectures, we will discuss fundamental concepts of Bayesian inference and its applications to dynamical systems in the context of data assimilation. The focus is on the assimilation step, which combines observation data with a prediction model. The lectures will discuss Gaussian and non-Gaussian approaches, including Kalman and particle filters. We will also discuss implementations of various algorithms and consider their computational benefits and efficiency. It is recommended to know basic numerical analysis for predictions, but it is optional to understand the main ideas of the lectures.

In this series of lectures, we will discuss fundamental concepts of Bayesian inference and its applications to dynamical systems in the context of data assimilation. The focus is on the assimilation step, which combines observation data with a prediction model. The lectures will discuss Gaussian and non-Gaussian approaches, including Kalman and particle filters. We will also discuss implementations of various algorithms and consider their computational benefits and efficiency. It is recommended to know basic numerical analysis for predictions, but it is optional to understand the main ideas of the lectures.