Programs and Notes

Programs:

 

GitHub: https://github.com/jjx323

 

Notes: Some notes for papers concerned with Bayesian inverse methods and inverse problems can be found on https://www.jianshu.com/u/d4eab548a4f9

 

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阅读书目:

 

1. 程士宏,测度论与概率论基础,北京大学出版社,本科生数学基础课教材,2004.

2. Giuseppe Da Prato, An Introduction to Infinite-Dimensional Analysis, Springer, 2006.

3. Giuseppe Da Prato and Jerzy Zabczyk, Stochastic Equations in Infinite Dimensions, Second Edition, Cambridge University Press, 2014.

4. Michael Reed and Barry Simon, Methods of Modern Mathematical Physics, Volume I, Functional Analysis,Elsevier, 2003.

5. Jari Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Applied Mathematical Sciences 160, Springer, 2004.

6. Masoumeh Dashti and Andrew M. Stuart, The Bayesian Approach to Inverse Problem, Handbook of Uncertainty Quantification, Springer International Publishing Switzerland 2017.

7. T. J. Sullivan, Introduction to Uncertainty Quantification, Texts in Applied Mathematics Volume 63, Springer, 2015.

8. Daniela Calvetti and Erkki Somersalo, Introduction to Bayesian Scientific Computing -- Ten Lectures on Subjective Computing, Springer, 2007.

9. Lawrence C. Evans, Partial Differential Equations, Second Edition, American Mathematical Society, 2010.

10. Lawrence C. Evans, An Introduction to Stochastic Differential Equations,  American Mathematical Society, 2014.

11. Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, Massachusetts, London, England, 2006.

12. Evarist Gine and Richard Nickl, Mathematical Foundations of Infinite-Dimensional Statistical Models, Cambridge University Press, 2016.