This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of the way these principles can be applied to modelling real-world systems. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.
2019-12-21 22:01:57
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美国Polytechnic University的Athanasios Papoulis教授的经典教材配套习题答案
2019-12-21 20:59:24
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随即过程滤波的经典教材，Jazwinski的代表作，欢迎大家下载
2019-12-21 20:34:13
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Stochastic Processes and Filtering Theory.pdf
2019-12-21 20:13:31
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《随机过程导论》，英文名《An introduction to stochastic processes》，Edward P.C. Kao 著，第一部分。
2019-12-21 19:58:14
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《随机过程导论》，英文名《An introduction to stochastic processes》，Edward P.C. Kao 著，第二部分。请用DjView打开。
2019-12-21 19:58:14
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通信理论第一泰斗Gallager著作，随机过程工科学生最好的教材，适合各工科学生学习随机过程相关知识。
2019-12-21 19:45:29
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A brief introduction to the formulation of various types of stochastic epidemic models is presented based on the well-known deterministic SIS and SIR epidemic models. Three different types of stochastic model formulations are discussed: discrete time Markov chain, continuous time Markov chain and stochastic differential equations. Properties unique to the stochastic models are presented: probability of disease extinction, probability of disease outbreak, quasistationary probability distribution, final size distribution, and expected duration of an epidemic. The chapter ends with a discussion of two stochastic formulations that cannot be directly related to the SIS and SIR epidemic models. They are discrete time Markov chain formulations applied in the study of epidemics within households (chain binomial models) and in the prediction of the initial spread of an epidemic (branching processes).
2019-12-21 19:24:08
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Stochastic Processes, Theory for Applications Robert G. Gallager著的随机过程的习题解答
2019-12-21 19:23:30
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