Fundamentals of Stochastic Networks
Published on 26. August 2011
Software
Other digital
312 pages
978-1-118-09297-2 (ISBN)
Description
This book uniquely combines queuing networks with Bayesian, Boolean, and random networks and provides an interdisciplinary approach by including applications in biology, communications, mathematics, engineering, and operations management. Combining techniques from stochastic processes and graph theory to analyze the behavior of networks, the book includes science and engineering applications, many practical examples that include related mathematical details, and numerous solved examples. It serves as both a reference for researchers and network professionals and as an upper-undergraduate and graduate-level coursebook.
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Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 250 mm
Width: 150 mm
Thickness: 15 mm
Weight
666 gr
ISBN-13
978-1-118-09297-2 (9781118092972)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Person
OLIVER C. IBE , ScD, is Associate Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts at Lowell. He has more than thirty years of experience in academia and the telecommunications industry in various technical and management capacities. Dr. Ibe's research interests include stochastic systems modeling, bioinformatics, and communication network performance modeling. He is the author of Converged Network Architectures: Delivering Voice over IP, ATM, and Frame Relay (Wiley).
Content
Preface xi Acknowledgments, xii 1 Basic Concepts in Probability 1 1.1 Introduction, 1 1.2 Random Variables, 1 1.3 Transform Methods, 5 1.4 Covariance and Correlation Coefficient, 8 1.5 Sums of Independent Random Variables, 8 1.6 Random Sum of Random Variables, 9 1.7 Some Probability Distributions, 11 1.8 Limit Theorems, 21 2 Overview of Stochastic Processes 26 2.1 Introduction, 26 2.2 Classification of Stochastic Processes, 27 2.3 Stationary Random Processes, 27 2.4 Counting Processes, 28 2.5 Independent Increment Processes, 29 2.6 Stationary Increment Process, 29 2.7 Poisson Processes, 30 2.8 Renewal Processes, 32 2.9 Markov Processes, 37 2.10 Gaussian Processes, 56 3 Elementary Queueing Theory 61 3.1 Introduction, 61 3.2 Description of a Queueing System, 61 3.3 The Kendall Notation, 64 3.4 The Little's Formula, 65 3.5 The M/M/1 Queueing System, 66 3.6 Examples of Other M/M Queueing Systems, 71 3.7 M/G/1 Queue, 79 4 Advanced Queueing Theory 93 4.1 Introduction, 93 4.2 M/G/1 Queue with Priority, 93 4.3 G/M/1 Queue, 99 4.4 The G/G/1 Queue, 105 4.5 Special Queueing Systems, 109 5 Queueing Networks 124 5.1 Introduction, 124 5.2 Burke's Output Theorem and Tandem Queues, 126 5.3 Jackson or Open Queueing Networks, 128 5.4 Closed Queueing Networks, 130 5.5 BCMP Networks, 132 5.6 Algorithms for Product-Form Queueing Networks, 138 5.7 Networks with Positive and Negative Customers, 144 6 Approximations of Queueing Systems and Networks 150 6.1 Introduction, 150 6.2 Fluid Approximation, 151 6.3 Diffusion Approximations, 155 7 Elements of Graph Theory 172 7.1 Introduction, 172 7.2 Basic Concepts, 172 7.3 Connected Graphs, 177 7.4 Cut Sets, Bridges, and Cut Vertices, 177 7.5 Euler Graphs, 178 7.6 Hamiltonian Graphs, 178 7.7 Trees and Forests, 179 7.8 Minimum Weight Spanning Trees, 181 7.9 Bipartite Graphs and Matchings, 182 7.10 Independent Set, Domination, and Covering, 186 7.11 Complement of a Graph, 188 7.12 Isomorphic Graphs, 188 7.13 Planar Graphs, 189 7.14 Graph Coloring, 191 7.14.1 Edge Coloring, 191 7.14.2 The Four-Color Problem, 192 7.15 Random Graphs, 192 7.16 Matrix Algebra of Graphs, 195 7.17 Spectral Properties of Graphs, 198 7.18 Graph Entropy, 201 7.19 Directed Acyclic Graphs, 201 7.20 Moral Graphs, 202 7.21 Triangulated Graphs, 202 7.22 Chain Graphs, 203 7.23 Factor Graphs, 204 8 Bayesian Networks 209 8.1 Introduction, 209 8.2 Bayesian Networks, 210 8.3 Classification of BNs, 214 8.4 General Conditional Independence and d -Separation, 215 8.5 Probabilistic Inference in BNs, 215 8.6 Learning BNs, 227 8.7 Dynamic Bayesian Networks, 231 9 Boolean Networks 235 9.1 Introduction, 235 9.2 Introduction to GRNs, 236 9.3 Boolean Network Basics, 236 9.4 Random Boolean Networks, 238 9.5 State Transition Diagram, 239 9.6 Behavior of Boolean Networks, 240 9.7 Petri Net Analysis of Boolean Networks, 242 9.8 Probabilistic Boolean Networks, 250 9.9 Dynamics of a PBN, 251 9.10 Advantages and Disadvantages of Boolean Networks, 252 10 Random Networks 255 10.1 Introduction, 255 10.2 Characterization of Complex Networks, 256 10.3 Models of Complex Networks, 261 10.4 Random Networks, 265 10.5 Random Regular Networks, 267 10.6 Consensus over Random Networks, 268 10.7 Summary, 274 References 276 Index 280