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Titlebook: Stochastic Population Theories; Donald Ludwig Book 1974 Springer-Verlag Berlin · Heidelberg 1974 Branching process.Derivation.Galton-Watso

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發(fā)表于 2025-3-21 16:49:40 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Stochastic Population Theories
編輯Donald Ludwig
視頻videohttp://file.papertrans.cn/879/878090/878090.mp4
叢書名稱Lecture Notes in Biomathematics
圖書封面Titlebook: Stochastic Population Theories;  Donald Ludwig Book 1974 Springer-Verlag Berlin · Heidelberg 1974 Branching process.Derivation.Galton-Watso
描述These notes serve as an introduction to stochastic theories which are useful in population biology; they are based on a course given at the Courant Institute, New York, in the Spring of 1974. In order to make the material. accessible to a wide audience, it is assumed that the reader has only a slight acquaintance with probability theory and differential equations. The more sophisticated topics, such as the qualitative behavior of nonlinear models, are approached through a succession of simpler problems. Emphasis is placed upon intuitive interpretations, rather than upon formal proofs. In most cases, the reader is referred elsewhere for a rigorous development. On the other hand, an attempt has been made to treat simple, useful models in some detail. Thus these notes complement the existing mathematical literature, and there appears to be little duplication of existing works. The authors are indebted to Miss Jeanette Figueroa for her beautiful and speedy typing of this work. The research was supported by the National Science Foundation under Grant No. GP-32996X3. CONTENTS I. LINEAR MODELS ?????. ???????????????. . ??????????????????????????????????????? 1 1. The Poisson Process ?????
出版日期Book 1974
關鍵詞Branching process; Derivation; Galton-Watson process; Poisson process; Population; Probability theory; Sto
版次1
doihttps://doi.org/10.1007/978-3-642-80883-8
isbn_softcover978-3-540-07010-8
isbn_ebook978-3-642-80883-8Series ISSN 0341-633X Series E-ISSN 2196-9981
issn_series 0341-633X
copyrightSpringer-Verlag Berlin · Heidelberg 1974
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沙發(fā)
發(fā)表于 2025-3-21 21:34:22 | 只看該作者
Donald Ludwigbecause of lack of funding. FESTLIP’s fifth season had been canceled, she explained, because the Brazilian Ministry of Culture and FUNARTE had stopped providing financial support to the festival in 2011, leaving Pires’s production company, TALU Produ??es, with substantial debt. This was a vastly dif
板凳
發(fā)表于 2025-3-22 00:53:45 | 只看該作者
Donald Ludwigroduced and.give their views on the improvements that are thereby achieved. In other words, they.assess to what extent the amendments have mitigated the disadvantages of the previous.Insolvency Regulation..Three of the chapters concentrate on the issues pertaining to jurisdiction, such as.the proble
地板
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發(fā)表于 2025-3-22 08:47:25 | 只看該作者
Donald Ludwigand cost monotonicity for constrained and non-linear systems.Receding Horizon Control. (RHC) introduces the essentials of a successful feedback strategy that has emerged in many industrial fields. RHC has several advantages over other types of control: greater adaptability to parametric changes than
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發(fā)表于 2025-3-22 19:04:19 | 只看該作者
Dynamical Systems Perturbed by Noise,hout prior knowledge of what the weather will be. Symbolically,.where R is a function of t which represents the effects of the weather. If a number of populations are observed at different times or different places, then R will be a different function in each case. Since R cannot be predicted in any
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發(fā)表于 2025-3-22 22:34:33 | 只看該作者
Book 1974stitute, New York, in the Spring of 1974. In order to make the material. accessible to a wide audience, it is assumed that the reader has only a slight acquaintance with probability theory and differential equations. The more sophisticated topics, such as the qualitative behavior of nonlinear models
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發(fā)表于 2025-3-23 03:19:48 | 只看該作者
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發(fā)表于 2025-3-23 06:34:36 | 只看該作者
Dynamical Systems Perturbed by Noise, case, the problem appears to be unsolvable. However, certain statistics of the weather can be predicted from past data. This suggests that R be considered to be a random process whose moments, say, are known. One would like to describe the distribution of X in terms of the moments of R.
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