Daniel Sanz-Alonso is Assistant Professor in the Committee on Computational and Applied Mathematics within the Department of Statistics at the University of Chicago. His contributions to inverse problems and data assimilation have been recognized with a José Luis Rubio de Francia prize and an NSF CAREER award. Andrew Stuart is Professor in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Sciences at Caltech. He is well known for his work in applied and computational mathematics, in the areas of dynamical systems, inverse problems, data assimilation, and machine learning. Armeen Taeb is Assistant Professor in the Department of Statistics at the University of Washington. His work focuses on developing efficient methods for graphical modeling and latent-variable modeling, learning causal relations from data, and model selection in contemporary data analysis settings. His PhD thesis received the W. P. Carey & Co. Prize for outstanding dissertation in applied mathematics.

• Introduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.

Daniel Sanz-Alonso is Assistant Professor in the Committee on Computational and Applied Mathematics within the Department of Statistics at the University of Chicago. His contributions to inverse problems and data assimilation have been recognized with a José Luis Rubio de Francia prize and an NSF CAREER award. Andrew Stuart is Professor in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Sciences at Caltech. He is well known for his work in applied and computational mathematics, in the areas of dynamical systems, inverse problems, data assimilation, and machine learning. Armeen Taeb is Assistant Professor in the Department of Statistics at the University of Washington. His work focuses on developing efficient methods for graphical modeling and latent-variable modeling, learning causal relations from data, and model selection in contemporary data analysis settings. His PhD thesis received the W. P. Carey & Co. Prize for outstanding dissertation in applied mathematics.

• Introduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.

Daniel Sanz-Alonso is Assistant Professor in the Committee on Computational and Applied Mathematics within the Department of Statistics at the University of Chicago. His contributions to inverse problems and data assimilation have been recognized with a José Luis Rubio de Francia prize and an NSF CAREER award. Andrew Stuart is Professor in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Sciences at Caltech. He is well known for his work in applied and computational mathematics, in the areas of dynamical systems, inverse problems, data assimilation, and machine learning. Armeen Taeb is Assistant Professor in the Department of Statistics at the University of Washington. His work focuses on developing efficient methods for graphical modeling and latent-variable modeling, learning causal relations from data, and model selection in contemporary data analysis settings. His PhD thesis received the W. P. Carey & Co. Prize for outstanding dissertation in applied mathematics.

• Introduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.