Mark M.Meerschaert美国密歇根州立大学概率统计系主任,内华达大学物理系教授。他曾在密歇根大学、英格兰学院、新西兰达尼丁Otago大学执教。讲授过数学建模、概率、统计学、运筹学、偏微分方程、地下水及地表水水文学与统计物理学课程。他当前的研究方向包括无限方差概率模型的极限定理和参数估计、金融数学中的厚尾模型、用厚尾模型及周期协方差结构建模河水流、异常扩散、连续时间随机流动、分数次导数和分数次偏微分方程、地下水流及运输。
目录
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PrefaceⅠ OPTIMIZATION MODELS1 ONE VARIABLE OPTIMIZATION 1.1 The Five-Step Method 1.2 Sensitivity Analysis 1.3 Sensitivity and Robustness 1.4 Exercises2 MULTIVARIABLE OPTIMIZATION 2.1 Unconstrained Optimization 2.2 Lagrange Multipliers 2.3 Sensitivity Analysis and Shadow Prices 2.4 Exercises3 COMPUTATIONAL METHODS FOR OPTIMIZATION 3.1 One Variable Optimization 3.2 Multivariable Optimization 3.3 Linear Programming 3.4 Discrete Optimization 3.5 ExercisesⅡ DYNAMIC MODELS4 INTRODUCTION TO DYNAMIC MODELS 4.1 Steady State Analysis 4.2 Dynamical Systems 4.3 Discrete Time Dynamical Systems 4.4 Exercises5 ANALYSIS OF DYNAMIC MODELS 5.1 Eigenvalue Methods 5.2 Eigenvalue Methods for Discrete Systems 5.3 Phase Portraits 5.4 Exercises6 SIMULATION OF DYNAMIC MODELS 6.1 Introduction to Simulation 6.2 Continuous-Time Models 6.3 The Euler Method 6.4 Chaos and Fractals 6.5 ExercisesⅢ PROBABILITY MODELS7 INTRODUCTION TO PROBABILITY MODELS 7.1 Discrete Probability Models 7.2 Continuous Probability Models 7.3 Introduction to Statistics 7.4 Diffusion 7.5 Exercises8 STOCHASTIC MODELS 8.1 Markov Chains 8.2 Markov Processes 8.3 Linear Regression 8.4 Time Series 8.5 Exercises9 SIMULATION OF PROBABILITY MODELS 9.1 Monte Carlo Simulation 9.2 The Markov Property 9.3 Analytic Simulation 9.4 ExercisesAfterwordIndex
PrefaceⅠ OPTIMIZATION MODELS1 ONE VARIABLE OPTIMIZATION 1.1 The Five-Step Method 1.2 Sensitivity Analysis 1.3 Sensitivity and Robustness 1.4 Exercises2 MULTIVARIABLE OPTIMIZATION 2.1 Unconstrained Optimization 2.2 Lagrange Multipliers 2.3 Sensitivity Analysis and Shadow Prices 2.4 Exercises3 COMPUTATIONAL METHODS FOR OPTIMIZATION 3.1 One Variable Optimization 3.2 Multivariable Optimization 3.3 Linear Programming 3.4 Discrete Optimization 3.5 ExercisesⅡ DYNAMIC MODELS4 INTRODUCTION TO DYNAMIC MODELS 4.1 Steady State Analysis 4.2 Dynamical Systems 4.3 Discrete Time Dynamical Systems 4.4 Exercises5 ANALYSIS OF DYNAMIC MODELS 5.1 Eigenvalue Methods 5.2 Eigenvalue Methods for Discrete Systems 5.3 Phase Portraits 5.4 Exercises6 SIMULATION OF DYNAMIC MODELS 6.1 Introduction to Simulation 6.2 Continuous-Time Models 6.3 The Euler Method 6.4 Chaos and Fractals 6.5 ExercisesⅢ PROBABILITY MODELS7 INTRODUCTION TO PROBABILITY MODELS 7.1 Discrete Probability Models 7.2 Continuous Probability Models 7.3 Introduction to Statistics 7.4 Diffusion 7.5 Exercises8 STOCHASTIC MODELS 8.1 Markov Chains 8.2 Markov Processes 8.3 Linear Regression 8.4 Time Series 8.5 Exercises9 SIMULATION OF PROBABILITY MODELS 9.1 Monte Carlo Simulation 9.2 The Markov Property 9.3 Analytic Simulation 9.4 ExercisesAfterwordIndex
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1 有用 富兰克林顿 2012-07-11 14:20:13
很好的数学建模入门教材。其特点是重在训练“modelling”而不是介绍“model”,并将灵敏性分析贯穿全书,意在警告建模者:你建立的模型不一定是稳定的。////缺点:缺乏反面例子说明灵敏性分析的重要性。其次,练手的、机械的习题多,启发性强的习题还不够多。还有这本教材有个讨厌的地方有部分习题的表述是模棱两可的,让人无所适从。
0 有用 舍與 2013-08-03 21:41:09
建模入門指導. 不太強調算法, 更強調建模過程和方法.
0 有用 Dz 2010-12-07 18:53:26
Page 282, Figure 8.14 有问题,同时统计建模的内容再多一点就更好了。
0 有用 cokuma 2011-03-27 12:54:44
看的是第2版,比较浅入深出