### Recommended books

*Monte Carlo Methods*, by Malvin Kalos and Paula Whitlock: An excellent physical introduction to basic Monte Carlo. It is elementary but surprisingly accurate mathematically.

*Monte Carlo Strategies in Scientific Computing*, Jun Liu: More advanced discussion and applications beyond statistical physics. It has a good discussion of sequential sampling and particle filtering.

*Lectures on Monte Carlo Methods*, Neal Madras: More advanced discussion and applications beyond statistical physics. It has a good discussion of sequential sampling and particle filtering.

*Monte Carlo Methods*, Neal Madras (editor): An excellent collection of survey articles by practitioners. It has a particularly good entry on the multi-histogram method and simulated tempering for physical applications.

*Large Deviations and Applications*, S. R. S. Varadhan: The best possible introduction to large deviation theory. More theoretical than we need for this course.

*Large Deviation Techniques and Applications*, Amir Dembo and Ofer Zeitouni: An easy (as possible) introduction to large deviation theory with some discussion of its use in Monte Carlo.

*Stochastic Simulation : Algorithms and Analysis*, Soren Asmussen and Peter W. Glynn: A great resource for stochastic approximation algorithms.

### Class Lecture Notes

- To be posted

### Web resources

Famous yet unpublished lecture notes by Alan Sokal on Monte Carlo. They have a long discussion of error bars for MCMC and some collective mode methods, including Swendsen Wang and multigrid Monte Carlo.Survey by Santosh Vempala of the Lovasz school work on Monte Carlo. It has an excellent discussion of their beautiful version of Cheeger's inequality.

Page last updated: July 1, 2010