| 提供了分析模拟数据及模拟模型的拟合检验所需的统计方法；通过许多实用的例子（如多服务器排队法、存货控制及行使股票期权等）来阐明和提出理论；强调方差缩减技术，包括控制变量及它们在回归分析中的应用等；提供了有关保险风险模型、生成随机向量、奇异期权的材料和关于产生离散随机变量混淆方法的独特材料；第4版特别增加了随机序列函数和随机子集函数的评估、分层抽样法的应用。... |
| Sheldon M. Ross国际知名概率与统计学家，南加州大学工业工程与运筹系系主任。毕业于斯坦福大学统计系，曾在加州大学伯克利分校任教多年。研究领域包括：随机模型．仿真模拟、统计分析、金融数学等：Ross教授著述颇丰，他的多种畅销数学和统计教材均产生了世界性的影响，如Introduction to Probability Models(《应用随机过程：概率模型导论》)，A First Course in Probability(《概率论墓础 教程》)等(均由人民邮电出版社出版)。 .. << 查看详细 |
| 1 introduction exercises 2 elements of probability 2.1 sample space and events 2.2 axioms of probability 2.3 conditional probability and independence 2.4 random variables 2.5 expectation 2.6 variance 2.7 chebyshev's inequality and the laws of large numbers 2.8 some discrete random variables 2.9 continuous random variables 2.10 conditional expectation and conditional variance 3 random numbers 3.1 pseudorandom number generation 3.2 using random numbers to evaluate integrals 4 generating discrete random variables 4.1 the inverse transform method 4.2 generating a poisson random variables 4.3 generating binomial random variables .4.4 the acceptance-rejection technique 4.5 the compositon approach 4.6 generating random vectors 5 generating continuous random variables 5.1 the inverse transform algorithm 5.2 the rejecton method 5.3 the polar method for generating normal random variables 5.4 generating a poissn process 5.5 generating a nonhomogeneous poisson process 6 the discrete event simulation approach 7 statistical analysis of simulated data 8 variance reduction techniques 9 statistical validation techniques 10 markov chain monte carlo methods 11 some additional topics index |
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