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Titlebook: Compartmental Modeling with Networks; Gilbert G. Walter,Martha Contreras Book 1999 Birkhäuser Boston 1999 Applied math.Maple.Markov.mathem

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书目名称Compartmental Modeling with Networks
编辑Gilbert G. Walter,Martha Contreras
视频video
丛书名称Modeling and Simulation in Science, Engineering and Technology
图书封面Titlebook: Compartmental Modeling with Networks;  Gilbert G. Walter,Martha Contreras Book 1999 Birkhäuser Boston 1999 Applied math.Maple.Markov.mathem
描述The subject of mathematical modeling has expanded considerably in the past twenty years. This is in part due to the appearance of the text by Kemeny and Snell, "Mathematical Models in the Social Sciences," as well as the one by Maki and Thompson, "Mathematical Models and Applica­ tions. " Courses in the subject became a widespread if not standard part of the undergraduate mathematics curriculum. These courses included var­ ious mathematical topics such as Markov chains, differential equations, linear programming, optimization, and probability. However, if our own experience is any guide, they failed to teach mathematical modeling; that is, few students who completed the course were able to carry out the mod­ eling paradigm in all but the simplest cases. They could be taught to solve differential equations or find the equilibrium distribution of a regular Markov chain, but could not, in general, make the transition from "real world" statements to their mathematical formulation. The reason is that this process is very difficult, much more difficult than doing the mathemat­ ical analysis. After all, that is exactly what engineers spend a great deal of time learning to do. But they con
出版日期Book 1999
关键词Applied math; Maple; Markov; mathematical modeling; model; modeling
版次1
doihttps://doi.org/10.1007/978-1-4612-1590-5
isbn_softcover978-1-4612-7207-6
isbn_ebook978-1-4612-1590-5Series ISSN 2164-3679 Series E-ISSN 2164-3725
issn_series 2164-3679
copyrightBirkhäuser Boston 1999
The information of publication is updating

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https://doi.org/10.1007/978-3-030-85462-1In Chapter 7, we saw how weighted digraphs and adjacency matrices are related. In this chapter, we consider particular types of weights and matrices that are used with Markov chains, and their associated special terms, which differ from those used previously.
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https://doi.org/10.1007/978-3-031-32910-4We assume that we have a Markov chain with transition matrix . and stochastic digraph ., as described in the last chapter. The digraph can be assumed to be weakly connected since, otherwise, the chain can be split into several noninteracting parts.
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Qilong Wang,Hongmei Chen,Lizhen WangThe prototypes of the absorbing chains are the Russian roulette and random walk chains:
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Xiangfu Meng,Weipeng Xie,Jiangyan CuiIn this chapter, we first present elements of the theory of compartmental models. We then present a few special cases and examples and examine the structure of the associated matrices.
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