mark m.meerschaert,美国密歇根州立大学概率统计系教授。他曾在密歇根大学、英格兰学院、内华达大学、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、医学成像、异常扩散、连续时间随机游动、分数次导数和分数次偏微分方程、地下水流及运输。
【目录】
preface i optimization models 1 one variable optimization 1.1 the five-step method 1.2 sensitivity analysis 1.3 sensitivity and robustness 1.4 exercises 2 multivariable optimization 2.1 unconstrained optimization 2.2 lagrange multipliers 2.3 sensitivity analysis and shadow prices 2.4 exercises 3 computational methods for optimization 3.1 one variable optimization 3.2 multivariable optimization 3.3 linear programming 3.4 discrete optimization 3.5 exercises ii dynamic models 4 introduction to dynamic models 4.1 steady state analysis 4.2 dynamical systems 4.3 discrete time dynamical systems 4.4 exercises 5 analysis of dynamic models 5.1 eigenvalue methods 5.2 eigenvalue methods for discrete systems 5.3 phase portraits 5.4 exercises 6 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 exercises iii probability‘ models. 7 introduction to probabiuty models 7.1 discrete probability models 7.2 continuous probability models 7.3 introduction to statistics 7.4 diffusion 7.5 exercises 8 stochastic models 8.1 markov chains 8.2 markov processes 8.3 linear regression 8.4 time series 8.5 exercises 9 simulation of probability models 9.1 monte carlo simulation 9.2 the markov property 9.3 analytic simulation 9.4 particle tracking 9.5 fractional diffusion 9.6 exercises afterword index
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