| 期刊全稱 | An Introduction to Applied Probability | | 影響因子2023 | Pierre Brémaud | | 視頻video | http://file.papertrans.cn/156/155137/155137.mp4 | | 發(fā)行地址 | Provides a clear and intuitive introduction to the range of probabilistic tools used in the applied sciences.Covers diverse topics such as random graphs, rejection sampling, and Bayesian hypothesis te | | 學(xué)科分類 | Texts in Applied Mathematics | | 圖書封面 |  | | 影響因子 | This book provides the elements of probability and stochastic processes of direct interest to the applied sciences where probabilistic models play an important role, most notably in the information and communications sciences, computer sciences, operations research, and electrical engineering, but also in fields like epidemiology, biology, ecology, physics, and the earth sciences..The theoretical tools are presented gradually, not deterring the readers with a wall of technicalities before they have the opportunity to understand their relevance in simple situations. In particular, the use of the so-called modern integration theory (the Lebesgue integral) is postponed until the fifth chapter, where it is reviewed in sufficient detail for a rigorous treatment of the topics of interest in the various domains of application listed above..The treatment, while mathematical, maintains a balance between depth and accessibility that is suitable for theefficient manipulation, based on solid theoretical foundations, of the four most important and ubiquitous categories of probabilistic models:.Markov chains, which are omnipresent and versatile models in applied probability.Poisson processes (on | | Pindex | Textbook 2024 |
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