Βιβλιογραφία

1. Stochastic Differential Equations, An Introduction with Applications, B. Oksendal, Springer, 2003

2. Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations, Springer, G. A. Pavliotis, 2014

3. Handbook of stochastic methods: For Physics, Chemistry and the Natural Sciences
   C. W., Gardiner, Springer, 2004

4. All of Statistics, A Concise Course in Statistical Inference L. Wasserman, Springer 2013

5. Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, The MIT Press; 1 2012

6. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer 2006

7. Simulation and Inference for Stochastic Differential Equations, S. Iacus, Springer, 2008

8. Parameter estimation for multiscale diffusions: An overview. G. A. Pavliotis, Y. Pokern, and A.M. Stuart, in Statistical Methods for Stochastic Differential Equations, Chapman & Hall/CRC Monographs on Statistics and Applied Probability, 2012

9. Estimating functions for diffusion-type processes, M.l Sørensen, in Statistical Methods for Stochastic Differential Equations, Chapman & Hall/CRC Monographs on Statistics and Applied Probability, 2012

10. Path-space variational inference for non-equilibrium coarse-grained systems, V. Harmandaris, E. Kalligiannaki, M. Katsoulakis, P. Plecháč, Journal of Computational Physics, Volume 314, 016, Pages 355-383.

11. Introduction to Uncertainty Quantification, T. Sullivan, Springer, Texts in Applied Mathematics, 2019