具體描述
《統計模擬(英文版•第4版)》係統闡述瞭統計模擬的一些實用方法和技術。在對概率的基本知識進行瞭簡單的迴顧之後，介紹如何利用計算機産生隨機數以及如何利用這些隨機數産生任意分布的隨機變量、隨機過程等。然後討論瞭一些分析統計數據的方法和技術。如Bootstrap（自助法）、方差縮減技術等。接著講述瞭如何利用統計模擬來判斷所選的隨機模型是否擬閤實際的數據。最後介紹MCMC及一些最新發展的統計模擬技術和論題，如隨機序列函數和隨機子集函數的評估。《統計模擬(英文版•第4版)》在每章的最後還提供瞭不同難度的習題。
《統計模擬(英文版•第4版)》可作為高等院校數學、統計學、科學計算、保險學、精算學等專業的教材，也可供工程技術人員和應用工作者參考。
著者簡介
圖書目錄
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
· · · · · · (收起)
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
· · · · · · (收起)
讀後感
評分
評分
評分
評分
評分
用戶評價
评分
通俗易懂。
评分翻瞭幾章，覺得選材挺好。
评分【科研帝論壇 keyandi.com】有此本書的英文原版電子書下載噢
评分翻瞭幾章，覺得選材挺好。
评分翻瞭幾章，覺得選材挺好。
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