Probabilistic forecasting and Bayesian data assimilation / Sebastian Reich and Colin Cotter.

Includes bibliographical references and index.

Preface;
1. Prologue: how to produce forecasts;
Part I. Quantifying Uncertainty:
2. Introduction to probability;
3. Computational statistics;
4. Stochastic processes;
5. Bayesian inference;
Part II. Bayesian Data Assimilation:
6. Basic data assimilation algorithms;
7. McKean approach to data assimilation;
8. Data assimilation for spatio-temporal processes;
9. Dealing with imperfect models;
References;
Index.

This book covers key ideas and concepts. Ideal introduction for graduate students in any field where Bayesian data assimilation is applied.