統(tǒng)計(jì)模擬

內(nèi)容概要

　　“……本書(shū)內(nèi)容豐富，不論作為教材還是參考書(shū)都非常值得推薦?！薄绹?guó)統(tǒng)計(jì)學(xué)報(bào)　　“本書(shū)是一本非常優(yōu)秀的教材，強(qiáng)調(diào)了計(jì)算機(jī)在模擬技術(shù)上的應(yīng)用。一定的概率和統(tǒng)計(jì)知識(shí)將有助于理解本書(shū)的精髓?！薄獊嗰R遜網(wǎng)上書(shū)店評(píng)論　　統(tǒng)計(jì)模擬是一門(mén)新興的統(tǒng)計(jì)學(xué)和計(jì)算機(jī)結(jié)合的學(xué)科，因其便利性和經(jīng)濟(jì)性而廣泛應(yīng)用于統(tǒng)計(jì)學(xué)、數(shù)學(xué)、精算科學(xué)、工程學(xué)、物理學(xué)等眾多領(lǐng)域，用以獲得精確而有效的解決方案?！　”緯?shū)是國(guó)際知名統(tǒng)計(jì)學(xué)家Sheldon M. Ross所著的經(jīng)典教材，已被加州大學(xué)伯克利分校、哥倫比亞大學(xué)等多所名校采用。書(shū)中涵蓋了統(tǒng)計(jì)模擬最新方法和技術(shù)，提供了豐富的實(shí)例，備受業(yè)界推崇?！　”緯?shū)特色：　　提供了分析模擬數(shù)據(jù)以及模擬模型的擬合檢驗(yàn)所需的統(tǒng)計(jì)方法?！　⊥ㄟ^(guò)許多實(shí)用的例子（如多服務(wù)器排隊(duì)法、存貨控制及行使股票期權(quán)等）來(lái)闡明和提出理論?！　?qiáng)調(diào)方差縮減技術(shù)，包括控制變量及它們?cè)谝驓w分析中的應(yīng)用等?！　√峁┝擞嘘P(guān)保險(xiǎn)風(fēng)險(xiǎn)模型、生成隨機(jī)向量、奇異期權(quán)的材料和關(guān)于產(chǎn)生離散隨機(jī)變量混淆方法的獨(dú)特材料?！　〉?版特別增加了隨機(jī)序列函數(shù)和隨機(jī)子集函數(shù)的評(píng)估、分層抽樣法的應(yīng)用?！　　”緯?shū)介紹了統(tǒng)計(jì)模擬的一些實(shí)用方法和技術(shù)。在對(duì)概率的基本知識(shí)進(jìn)行了簡(jiǎn)單的回顧這后，介紹了如何利用計(jì)算機(jī)產(chǎn)生隨機(jī)數(shù)以及如何利用這些隨機(jī)數(shù)產(chǎn)生任意分布的隨機(jī)變量、隨機(jī)過(guò)程等。然后介紹一些分析編譯數(shù)據(jù)的方法和技術(shù)，如Bootstrap、方差縮減技術(shù)等。接著介紹了如何利用統(tǒng)計(jì)模擬來(lái)判斷所選的隨機(jī)模型是否擬合實(shí)際的數(shù)據(jù)。最后介紹了MCMC及一些最新發(fā)展的統(tǒng)計(jì)模擬技術(shù)和論題。本書(shū)可作為統(tǒng)計(jì)學(xué)、計(jì)算數(shù)學(xué)、保險(xiǎn)學(xué)、精算學(xué)等專(zhuān)業(yè)本科生教材，也可供相關(guān)專(zhuān)業(yè)人士參考。本書(shū)為英文第4版。

作者簡(jiǎn)介

　　Sheldon,M.Ross，國(guó)際知名概率與統(tǒng)計(jì)學(xué)家，南加州大學(xué)工業(yè)工程民運(yùn)籌系系主任。畢業(yè)于斯坦福大學(xué)統(tǒng)計(jì)學(xué)，曾在加州大學(xué)伯克利分校任教多年。研究領(lǐng)域包括：隨機(jī)模型、仿真模擬、統(tǒng)計(jì)分析、金融數(shù)等。Ross教授著述頗豐，他的多種暢銷(xiāo)數(shù)學(xué)和統(tǒng)計(jì)教材均產(chǎn)生了世界性的影響，如Introduction to Probability Models（《應(yīng)用隨機(jī)過(guò)程：概率模型導(dǎo)論》），A Fisrt Course in Probability（《概率論基礎(chǔ)教程》）等（均由人民郵電出版社出版）。

書(shū)籍目錄

1 IntroductionExercises2 Elements of Probability2.1 Sample Space and Events2.2 Axioms of Probability2.3 Conditional Probability and Independence2.4 Random Variables2.5 Expectation2.6 Variance2.7 Chebyshevs Inequality and the Laws of Large Numbers2.8 Some Discrete Random VariablesBinomial Random VariablesPoisson Random VariablesGeometric Random VariablesThe Negative Binomial Random VariableHypergeometric Random Variables2.9 Continuous Random VariablesUniformly Distributed Random VariablesNormal Random VariablesExponential Random VariablesThe Poisson Process and Gamma Random VariablesThe Nonhomogeneous Poisson Process2.10 Conditional Expectation and Conditional VarianceExercisesReferences3 Random NumbersIntroduction3.1 Pseudorandom Number Generation3.2 Using Random Numbers to Evaluate IntegralsExercisesReferences4 Generating Discrete Random Variables4.1 The Inverse Transform Method4.2 Generating a Poisson Random Variable4.3 Generating Binomial Random Variables4.4 The Acceptance-Rejection Technique4.5 The Composition Approach4.6 Generating Random VectorsExercises5 Generating Continuous Random VariablesIntroduction5.1 The Inverse Transform Algorithm5.2 The Rejection Method5.3 The Polar Method for Generating Normal Random Variables5.4 Generating a Poisson Process5.5 Generating a Nonhomogeneous Poisson ProcessExercisesReferences6 The Discrete Event Simulation ApproachIntroduction6.1 Simulation via Discrete Events6.2 A Single-Server Queueing System6.3 A Queueing System with Two Servers in Series6.4 A Queueing System with Two Parallel Servers6.5 An Inventory Model6.6 An Insurance Risk Model6.7 A Repair Problem6.8 Exercising a Stock Option6.9 Verification of the Simulation ModelExercisesReferences7 Statistical Analysis of Simulated DataIntroduction7.1 The Sample Mean and Sample Variance7.2 Interval Estimates of a Population Mean7.3 The Bootstrapping Technique for Estimating Mean Square ErrorsExercisesReferences8 Variance Reduction TechniquesIntroduction8.1 The Use of Antithetic Variables8.2 The Use of Control Variates8.3 Variance Reduction by ConditioningEstimating the Expected Number of Renewals by Time t8.4 Stratified Sampling8.5 Importance Sampling8.6 Using Common Random Numbers8.7 Evaluating an Exotic OptionAppendix： Verification of Antithetic Variable ApproachWhen Estimating the Expected Value of Monotone FunctionsExercisesReferences9 Statistical Validation TechniquesIntroduction9.1 Goodness of Fit TestsThe Chi-Square Goodness of Fit Test for Discrete DataThe Kolmogorov-Smirnov Test for Continuous Data9.2 Goodness of Fit Tests When Some Parameters Are UnspecifiedThe Discrete Data CaseThe Continuous Data Case9.3 The Two-Sample Problem9.4 Validating the Assumption of a NonhomogeneousPoisson ProcessExercisesReferences10 Markov Chain Monte Carlo MethodsIntroduction10.1 Markov Chains10.2 The Hastings-Metropolis Algorithm10.3 The Gibbs Sampler10.4 Simulated Annealing10.5 The Sampling Importance Resampling AlgorithmExercisesReferences11 Some Additional TopicsIntroduction11.1 The Alias Method for Generating Discrete Random Variables11.2 Simulating a Two-Dimensional Poisson Process11.3 Simulation Applications of an Identity for Sums of Bernoulli Random Variables11.4 Estimating the Distribution and the Mean of the First Passage Time of a Markov Chain11.5 Coupling from the PastExercisesReferencesIndex

圖書(shū)封面

圖書(shū)標(biāo)簽Tags

無(wú)

評(píng)論、評(píng)分、閱讀與下載

---

    統(tǒng)計(jì)模擬 PDF格式下載

---