Monte Carlo Method的基本教材：
• G.S. Fishman, Monte Carlo Concepts, Algorithms, and Applications, Springer-Verlag, NY (1996).
• R. Y. Rubinstein, Simulation and the Monte Carlo Method, John Wiley & Sons, NY (1981).
• J. H. Halton, "A Retrospective and Prospective Survey of the Monte Carlo Method," SIAM Rev
12,1, (1970).
• C.P. Robert & G. Casella, Monte Carlo Statistical Methods, Springer (2004).

随机数发生器和随机抽样方法的参考书籍：
• L. Devroye, Non-Uniform Random Variate Generation, Springer-Verlag, NY (1986).
• D. E. Knuth, The Art of Computer Programming, Vol. 2: Semi-numerical Algorithms, 3rd Edition,
Addison-Wesley, Reading, MA (1998).
• J. von Neumann, "Various Techniques Used in Conjunction with Random Digits," J. Res. Nat.
Bur. Stand. Appl. Math Series 3, 36-38 (1951).
• C. J. Everett and E. D. Cashwell, "A Third Monte Carlo Sampler," LA9721-MS, Los Alamos
National Laboratory, Los Alamos, NM (1983).
• H. Kahn, "Applications of Monte Carlo," AECU-3259, Rand Corporation, Santa Monica, CA
(1954).
• E. J. McGrath and D. C. Irving, "Techniques for Efficient Monte Carlo Simulation," ORNL-RSIC-
38, Vols. I-III, Oak Ridge National Laboratory, Oak Ridge, TN (1975).
• J.E. Gentle, Random Number Generation and Monte Carlo Methods, Springer, NY (2003).
• F.B. Brown & Y. Nagaya, “The MCNP5 Random Number Generator”, Trans. Am. Nucl. Soc. [also,
LA-UR-02-3782] (November, 2002).
• F. B. Brown, "Random Number Generation with Arbitrary Strides," Trans. Am. Nucl. Soc. 71,202
(1994).
• F.B. Brown & W.R. Martin, “Direct Sampling of Monte Carlo Flight Paths in Media with
Continuously Varying Cross-sections”, ANS Topical Meeting on Mathematics & Computation,
Gatlinburg, TN April 6-11, 2003 [also, LA-UR-02-6530] (September, 2002).
• F. B. Brown & W. R. Martin, "Monte Carlo Particle Transport in Media with Exponentially Varying
Time-dependent Cross-sections", Trans. Am. Nucl. Soc [also, LA-UR-01-675] (June 2001).
• F. B. Brown and J. L. Vujic, "Comparison of Direct and Rejection Sampling Methods," Trans.Am.
Nucl. Soc. 69, 223 (Nov. 1993).

用R或S进行MC的比较好的介绍见Gentle，Springer in statistics系列。论坛里面有，但是没有被发掘出来。大家仔细找找，我就不给别人做广告了。

支持楼主！！MCM能否成为将来的主流呢？让我们拭目以待！不过，至少Bayesian Analysis算是很大的一个受益群体！

MCMC Tutorial

MCMC and Gibbs Sampling

MCMC for Statistical Inference

[此贴子已经被作者于2007-6-17 11:11:22编辑过]

amoybc兄弟所言极是。很多统计问题的发展并不在理论方面，而是问题怎么实现。比如，SV在度量股市波动性方面可以说效果并不比ARCH差。ARCH的流行与其实现起来较容易有很大关系。当然ARCH族的拟极大似然估计还有很大的发展空间。这一问题同样困扰很多新的统计方法的发展。传统的参数方法参数估计和推断是一个重大障碍，非参数分析的密度和回归模型的估计也亟待改进，其实现也的借助MCM。所以我认为在统计领域MCM很有发展潜力，它在自然科学领域的贡献那是有目共睹了。大家有兴趣看看这吧，这可是洛斯阿拉莫斯的MC讲义，不过是运用于粒子运输的，仅供统计同仁参考。

Title:FUNDAMENTALS OF MONTE CARLO PARTICLE TRANSPORT
Author(s):FORREST B. BROWN
Submitted to:Lecture notes for Monte Carlo course
Los Alamos National Laboratory。