References for the simulation portion of the course (March-May)

Luc Devroye's book "Non-Uniform Random Variate Generation" (online)
Robert, Christian P. and Casella, George (2004), "Monte Carlo statistical methods", Second Edition, Springer-Verlag Inc (Berlin; New York).
Note: comprehensive treatment of Monte Carlo methods (including MCMC, Importance Sampling and recently developed techniques) with plenty of related theoretical details. The level of the text is generally more advanced than the other two (notation is often measure-theoretic)
Gamerman, D. and Lopes, H.F.: Markov chain Monte Carlo: Stochastic Simulation for Bayesian Inference: This is a handy (and fairly concise) book on MCMC.
Gilks, W. R. (ed), Richardson, S. (ed), and Spiegelhalter, D. J.(ed) (1996), ``Markov chain Monte Carlo in practice'', Chapman & Hall Ltd (London; New York) Note: well written chapters by several different experts on a variety of topics from MCMC basics to specialized applications of MCMC. Since the field has evolved rapidly over the last 10 years this book is out of date. It does, however, have some useful information for first time MCMC users.
Liu, Jun S. (2001), "Monte Carlo strategies in scientific computing", Springer-Verlag Inc (Berlin; New York).
Note: this book has descriptions about a few advanced MCMC strategies along with a thorough treatment of Importance Sampling and Sequential Importance Sampling. It is probably too specialized for this course.

Givens and Hoeting (2005) "Computational Statistics" (Wiley): Chapters 6,7 and 8 (Ch.8 is on more advanced topics)
Gentle (2002) "Elements of Computational Statistics" (Springer): only Chapter 2 is really relevant for this course
Gentle (2003) "Elements of Computational Statistics" (Second Edition, Springer): only Chapter 4 is really relevant for this course
Thisted "Elements of Statistical Computing"
Ripley (1987) "Stochastic Simulation"
A useful (and entertaining!) read: Charlie Geyer's web page on "the bogosity of MCMC diagnostics"