Probabilistic Forecasting and Bayesian Data Assimilation

Probabilistic Forecasting and Bayesian Data Assimilation is written for graduate students in applied mathematics, computer science, engineering, geoscience, and other emerging applications areas. This book introduces students to the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. 

Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II.

MATLAB code is available to download from the publisher's website.