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ISBN:0521854423
Author: Ronald Meester,Massimo Franceschetti
ISBN13: 978-0521854429
Title: Random Networks for Communication: From Statistical Physics to Information Systems (Cambridge Series in Statistical and Probabilistic Mathematics)
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ePUB size: 1290 kb
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Language: English
Category: Mathematics
Publisher: Cambridge University Press; 1 edition (January 28, 2008)
Pages: 212

Random Networks for Communication: From Statistical Physics to Information Systems (Cambridge Series in Statistical and Probabilistic Mathematics) by Ronald Meester,Massimo Franceschetti



Massimo Franceschetti, Ronald Meester. Download (pdf, . 1 Mb) Donate Read. Epub FB2 mobi txt RTF. Converted file can differ from the original. If possible, download the file in its original format.

Massimo Franceschetti (Author). Series: Cambridge Series in Statistical and Probabilistic Mathematics (Book 24). Hardcover: 212 pages.

Series: Cambridge Series in Statistical and Probabilistic Mathematics. Information flow in random networks . c preliminaries . 1 Channel capacity . 2 Additive Gaussian channel . 3 Communication with continuous time signals . 4 c random networks . Scaling limits; single source–destination pair . Multiple source–destination pairs; lower bound .

Электронная книга "Random Networks for Communication: From Statistical Physics to Information Systems", Massimo Franceschetti, Ronald Meester. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Random Networks for Communication: From Statistical Physics to Information Systems" для чтения в офлайн-режиме.

by Massimo Franceschetti, Ronald Meester. Publisher: Cambridge University Press. The analysis of communication networks requires a fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow. This book is the first comprehensive introduction for graduate students and scientists to techniques and problems in the field of spatial random networks. The selection of material is driven by applications arising in engineering, and the treatment is both readable and mathematically rigorous

Massimo Franceschetti, Ronald Meester. om Networks for Communication. html?hl ru&id IEODOi9IilUC. Random Networks for Communication: From Statistical Physics to Information Systems Cambridge Series in Statistical and Probabilistic Mathematics (Том 24). Авторы.

Home All Categories Random Networks for Communication: From Statistical Physics to Information Systems (Cambridge Series in Statistical and Probabilistic Mathematics). ISBN13: 9780521854429. Random Networks for Communication : From Statistical Physics to Information Systems. by Massimo Franceschetti and Ronald Meester. When is a random network (almost) connected? How much information can it carry? How can you find a particular destination within the network? And how do you approach these questions - and others - when the network is random? The analysis of communication networks requires a fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow.

49 results in Cambridge Series in Statistical and Probabilistic Mathematics. Relevance Title Sorted by Date. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook.

Random networks for communication. From statistical physics to information systems. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge, 2007. ISBN 978-0-521-85442-9. Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press. Franceschetti, M. ; Meester, . Random networks for communication. Cambridge : Cambridge University Press, 2007. 056ec801954c, title "Random networks for communication", author "M. Franceschetti and . N1 - MR2398551 From statistical physics to information systems. T3 - Cambridge Series in Statistical and Probabilistic Mathematics. BT - Random networks for communication.

When is a random network (almost) connected? How much information can it carry? How can you find a particular destination within the network? And how do you approach these questions - and others - when the network is random? The analysis of communication networks requires a fascinating synthesis of random graph theory, stochastic geometry and percolation theory to provide models for both structure and information flow. This book is the first comprehensive introduction for graduate students and scientists to techniques and problems in the field of spatial random networks. The selection of material is driven by applications arising in engineering, and the treatment is both readable and mathematically rigorous. Though mainly concerned with information-flow-related questions motivated by wireless data networks, the models developed are also of interest in a broader context, ranging from engineering to social networks, biology, and physics.
Reviews: 3
Pettalo
Let me first mention about my background. I am neither a computer engineer nor a deep-core mathematician. I study applied mathematics to economics. I picked up this book because I am studying Random Networks since it is an important tool for my current research in Complex Social Networks. So, in term of contents, I cannot judge whether it is useful for a researcher in the field of communication engineering. The only thing I want to mention is that, as the authors have also mentioned in the preface, contents seem to be rather selective. Important classes of Random Network Models such as Erdos-Renyi Graph were not treated. But this is not a disadvantage,though. There are many other excellent references in Random graphs that readers can consult (e.g. Bollobas' Random Graphs and Durrett' random graph dynamics)

So what makes this book worth 5 stars? It is the excellent pedagogy that is applied throughout the book. I might sound a bit exaggerating. But in term of clarity in explanations and proofs, this is one of the best books I have read in years. Not only each proofs are elementary and highly detailed (which should be very helpful for students who are new to the field), they are very well-constructed, straightforward and "mathematically beautiful". There are also substantial discussions about intuitions and basic ideas behind important concepts. For these reasons I really enjoy reading this book. And I am sure that it will be appreciated by other readers too.

One caveat, though: this book does not have the solutions to the exercises. I believe that including them in the next edition should make this book even a more excellent one.
Dreladred
This book is about random network models and how local connectivity properties give rise to large scale properties that emerge as the network grows in size. The study of emergent properties of evolving, random structures, with most prominent that of random graphs, has been the focus of many researchers coming from widely diverse disciplines that include physics, mathematics, computer science as well as social sciences. The core idea behind all these studies is that a simple local connectivity rule that defines how two elements of the structure interact with each other can give rise to more complex connectivity properties that hold globally on the structure and manifest themselves (emerge) as the structure's size increases. Moreover, it appears that there is some critical point, or threshold value, for the local connectivity rule such that the properties emerge suddenly from non-existent to existent, when the rule crosses this point. These emergent properties are, then, aptly called threshold properties. The book is focused on the study of two elementary, but rich in properties and modelling power, combinatorial structures: the random tree and the random grid. In the random tree model, we have a tree composed of an infinite number of vertices each having k children, with k > 0. Also, a probability value p is fixed and then each edge of the tree appears in the tree, independently of the others, with probability p. In the random grid model, the nodes are positioned on the points of the two-dimensional integer grid. These models are in contrast with the classical pioneering Erdos-Renyi random graph models in which that in these models adjacency between two vertices is defined by physical proximity while in the latter adjacency can be potentially appear between any pair of vertices.

In addition to the theoretical exposition, each chapter is aptly complemented by exercises that, most often, encourage the reader to finish sketched or incomplete proofs given in the text. The exercises are carefully designed so as to be tractable, with some effort, and to increase, at the same time, the intuition and understanding of the reader of the similarities and differences between the various random graph models. Also, in the end of the book, the authors provide an Appendix with some useful background material on basic probability theory.

In summary, this book is a clear, readable and highly intuitive introduction to the properties and applications of random network models that, also, provides all the rigorous details or invites the reader to fill them in, in the exercises section. The models tackled by the authors are characterized by the important property that the geometry of the nodes has a pivotal role in the formation of the network connections, as opposed to classical Erdos-Renyi random graph models in which there is no notion of geometry and edges can be inserted (with some probability) between any pair of nodes. The balance between intuition and rigor is ideal, in my opinion, and reading the book is an enjoyable and highly rewarding endeavour. I believe this book will be useful to physicists, mathematicians, and computer scientists alike that look at random graph models where point locations affects the shape and properties of the resulting network: physicists will acquaint themselves with complex networks having rich modelling capabilities (e.g. models for random interaction particle systems such as spin glasses), mathematicians may discover connections of the networks with formal systems (much like the connection of the classical Erdos-Renyi random graph properties with first and second order logic), and computer scientists will greatly appreciate the applicability of the theory given in the book to
the study of realistic, ad-hoc mobile networks in which network node connections change rapidly and unpredictably as a function of the geometry of the current node positions.
Weernis
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