Analysis and Modelling of Non-Steady Flow in Pipe and Channel Networks

This book covers at an advanced level mathematical methods for analysis of telecommunication networks. The book concentrates on various call models used in telecommunications such as quality of service (QoS) in packet-switched Internet Protocol (IP) networks, Asynchronous Transfer Mode (ATM), and Time Division Multiplexing (TDM). Professionals, researchers, and graduate and advanced undergraduate students of telecommunications will benefit from this invaluable guidebook.

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Spatial point processes are mathematical models used to describe and analyse the geometrical structure of patterns formed by objects that are irregularly or randomly distributed in one-, two- or three-dimensional space. Examples include locations of trees in a forest, blood particles on a glass plate, galaxies in the universe, and particle centres in samples of material. Numerous aspects of the nature of a specific spatial point pattern may be described using the appropriate statistical methods. Statistical Analysis and Modelling of Spatial Point Patterns provides a practical guide to the use of these specialised methods. The application-oriented approach helps demonstrate the benefits of this increasingly popular branch of statistics to a broad audience. The book: Provides an introduction to spatial point patterns for researchers across numerous areas of application Adopts an extremely accessible style, allowing the non-statistician complete understanding Describes the process of extracting knowledge from the data, emphasising the marked point process Demonstrates the analysis of complex datasets, using applied examples from areas including biology, forestry, and materials science Features a supplementary website containing example datasets. Statistical Analysis and Modelling of Spatial Point Patterns is ideally suited for researchers in the many areas of application, including environmental statistics, ecology, physics, materials science, geostatistics, and biology. It is also suitable for students of statistics, mathematics, computer science, biology and geoinformatics.

This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems. The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an ergodic theoretical perspective is also provided, as well as spatial point processes. Using these basic tools, stability criteria, performance measures and comparison principles are obtained for a wide class of models, from the canonical M/M/1 and G/G/1 queues to more sophisticated systems, including the current “hot topics” of spatial radio networking, OFDMA and real-time networks. Contents 1. Introduction. Part 1: Discrete-time Modeling 2. Stochastic Recursive Sequences. 3. Markov Chains. 4. Stationary Queues. 5. The M/GI/1 Queue. Part 2: Continuous-time Modeling 6. Poisson Process. 7. Markov Process. 8. Systems with Delay. 9. Loss Systems. Part 3: Spatial Modeling 10. Spatial Point Processes.

While there are numerous books describing modern wireless communication systems that contain overviews of radio propagation and radio channel modelling, there are none that contain detailed information on the design, implementation and calibration of radio channel measurement equipment, the planning of experiments and the in depth analysis of measured data. The book would begin with an explanation of the fundamentals of radio wave propagation and progress through a series of topics, including the measurement of radio channel characteristics, radio channel sounders, measurement strategies, data analysis techniques and radio channel modelling. Application of results for the prediction of achievable digital link performance would be discussed with examples pertinent to single carrier, multi-carrier and spread spectrum radio links. This work would address specifics of communications in various different frequency bands for both long range and short range fixed and mobile radio links.

The second book in Darbyshire and Hampton’s Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB® takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB®. This allows for a more detailed analysis of some of the more computationally intensive and advanced topics, such as hedge fund classification, performance measurement and mean-variance optimisation. Darbyshire and Hampton’s first book in the series, Hedge Fund Modelling and Analysis Using Excel & and VBA, is seen as a valuable supplementary text to this book. Starting with an overview of the hedge fund industry the book then looks at a variety of commercially available hedge fund data sources. After covering key statistical techniques and methods, the book discusses mean-variance optimisation, hedge fund classification and performance with an emphasis on risk-adjusted return metrics. Finally, common hedge fund market risk management techniques, such as traditional Value-at-Risk methods, modified extensions and expected shortfall are covered. The book’s dedicated website, www.darbyshirehampton.com provides free downloads of all the data and MATLAB® source code, as well as other useful resources. Hedge Fund Modelling and Analysis Using MATLAB® serves as a definitive introductory guide to hedge fund modelling and analysis and will provide investors, industry practitioners and students alike with a useful range of tools and techniques for analysing and estimating alpha and beta sources of return, performing manager ranking and market risk management.

This book focuses heavily on the principles, analysis and applications of code-division multiple-access (CDMA) techniques in optical communication systems and networks. In this book, the authors intimately discuss modern optical networks and their applications in current and emerging communication technologies, evaluating the quality, speed and number of supported services. In particular, principles and fundamentals of optical CDMA techniques from beginner to advanced levels are heavily covered. Furthermore, the authors concentrate on methods and techniques of various encoding and decoding schemes and their structures, as well as analysis of optical CDMA systems with various transceiver models including advanced multi-level incoherent and coherent modulations with the architecture of access/aggregation networks in mind. Moreover, authors examine intriguing topics of optical CDMA networking, compatibility with IP networks, and implementation of optical multi-rate multi-service CDMA networks. Key features: Expanded coverage of optical CDMA networks, starts from principles and fundamentals Comprehensive mathematical modelling and analysis from signal to system levels Addresses the applications of modern optical networking in the current and emerging communication technologies Greater focus on advanced optical multi-level incoherent and coherent modulations, spreading codes, and transceiver designs Detailed hardware specifications, system-level block diagrams, and network nodes’ functionalities This book appeals to researchers, practicing engineers, and advanced students. It is a practical resource for readers with an interest in optical communications and networks.

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following: Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models…Each chapter is well-motivated by at least one carefully chosen example…demonstrating the broad applicability of mixed models in many different disciplines…most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.”—Kerrie Nelson University of South Carolina, in American Statistician, 2007

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following: Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models…Each chapter is well-motivated by at least one carefully chosen example…demonstrating the broad applicability of mixed models in many different disciplines…most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.”—Kerrie Nelson University of South Carolina, in American Statistician, 2007

The definitive book on Positioning Analysis – a powerful and sophisticated framework to help traders, investors and risk managers better understand commodity markets Positioning Analysis is a powerful framework to better understand commodity price dynamics, risk, and sentiment. It indicates what each category of trader is doing—what they are trading, how much they are trading and how they might behave under a variety of different circumstances. It is essential in isolating specific types of flow patterns, defining behavioral responses, measuring shifts in sentiment, and developing tools for better risk management. Advanced Positioning, Flow and Sentiment Analysis in Commodity Markets explains the fundamentals of Positioning Analysis and presents new concepts in Commodity Positioning Analytics. This invaluable guide helps readers recognize how certain types of positioning patterns can be used to develop models, indicators, and analyses that can be used to enhance performance. This updated second edition contains substantial new material, including analytics based on the analysis of flow, the decomposition of trading flows, trading activity in the Chinese commodity markets, and the inclusion of Newsflow into Positioning Analysis. Author: Mark J S Keenan, also covers the structure of positioning data, performance attribution of speculators, sentiment analysis and the identification of price risks and behavioral patterns that can be used to generate trading signals.. This must-have resource: Offers intuitive and accessible guidance to commodity market participants and risk managers at various levels and diverse areas of the market Provides a wide range of analytics that can be used directly or integrated into a variety of different commodity-related trading, investment, and risk management programs Features an online platform comprising a wide range of customizable, regularly-updated analytical tools Contains an abundance of exceptional graphics, charts, and illustrations Includes easy-to-follow instructions for building analytics. Advanced Positioning, Flow and Sentiment Analysis in Commodity Markets: Bridging Fundamental and Technical Analysis, 2 nd Edition is an indispensable source of information for all types of commodity traders, investors, and speculators, as well as investors in other asset classes who look to the commodity markets for price information.