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Simulation and Statistics with Excel: An Introduction to Business Students
by Luis Fernando IbarraThe use of simulation techniques has increased in importance in recent history, and simulation activities are an important resource for advanced preparation for the organization and execution of events. When formal mathematics is not enough, simulation may be the only option capable of approximating solutions. Simulation and Statistics with Excel: An Introduction to Business Students offers a non-rigorous and practical tour of the simulation procedure on computers, using a versatile and accessible resource, the Microsoft Excel spreadsheet. This book covers the concepts essential to understanding the basic principles and approaches of statistical simulation, allowing for the study of complex systems. Aimed at students in business and operational research beginning to use simulation as an instrument for understanding existing or proposed processes, this book will lay solid foundations in understanding simulation experimentation.Key Features: Provides a basis to understand the approaches and principles of simulator experiments. Uses a universal and easily accessible resource. Introduces simple examples to teach the fundamentals of simulation.
Simulation and Statistics with Excel: An Introduction to Business Students
by Luis Fernando IbarraThe use of simulation techniques has increased in importance in recent history, and simulation activities are an important resource for advanced preparation for the organization and execution of events. When formal mathematics is not enough, simulation may be the only option capable of approximating solutions. Simulation and Statistics with Excel: An Introduction to Business Students offers a non-rigorous and practical tour of the simulation procedure on computers, using a versatile and accessible resource, the Microsoft Excel spreadsheet. This book covers the concepts essential to understanding the basic principles and approaches of statistical simulation, allowing for the study of complex systems. Aimed at students in business and operational research beginning to use simulation as an instrument for understanding existing or proposed processes, this book will lay solid foundations in understanding simulation experimentation.Key Features: Provides a basis to understand the approaches and principles of simulator experiments. Uses a universal and easily accessible resource. Introduces simple examples to teach the fundamentals of simulation.
Smart Card Research and Advanced Applications: 22nd International Conference, CARDIS 2023, Amsterdam, The Netherlands, November 14–16, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14530)
by Shivam Bhasin Thomas RocheThis book constitutes the proceedings of the 22nd International Conference on Smart Card Research and Advanced Applications, CARDIS 2023, held in Amsterdam, The Netherlands, during November 14–16, 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 28 submissions. They were organized in topical sections as follows: fault attacks; side-channel analysis; smartcards & efficient Implementations; and side-channel & neural networks.
Soccer Analytics: An Introduction Using R (Chapman & Hall/CRC Data Science Series)
by Clive BeggsSports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors.Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can be analysed in real-life. The book addresses this issue via a practical route one approach designed to show readers how to successfully tackle a range of soccer related problems using the easy-to-learn computer language R. Through a series of easy-to-follow examples, the book explains how R can be used to: Download and edit soccer data Produce graphics and statistics Predict match outcomes and final league positions Formulate betting strategies Rank teams Construct passing networks Assess match play Soccer Analytics: An Introduction Using R is a comprehensive introduction to soccer analytics aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.
Soccer Analytics: An Introduction Using R (Chapman & Hall/CRC Data Science Series)
by Clive BeggsSports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors.Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can be analysed in real-life. The book addresses this issue via a practical route one approach designed to show readers how to successfully tackle a range of soccer related problems using the easy-to-learn computer language R. Through a series of easy-to-follow examples, the book explains how R can be used to: Download and edit soccer data Produce graphics and statistics Predict match outcomes and final league positions Formulate betting strategies Rank teams Construct passing networks Assess match play Soccer Analytics: An Introduction Using R is a comprehensive introduction to soccer analytics aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.
Social Research Methods: Qualitative, Quantitative and Mixed Methods Approaches
by Sigmund GronmoFraming research as the process of asking and answering questions, this book demonstrates how to identify good research questions and how to structure and explore them successfully. Whether you are just beginning your research journey or are a seasoned traveller, it helps you: • Decide what you want to achieve with your research • Know what options you have to explore your goals • Navigate the nuances of different research approaches • Understand the decisions of other researchers • Choose what path best suits your project. Through real-life examples demonstrating different types of research, the book introduces qualitative, quantitative, and mixed methods approaches so you can compare different methods at every stage of the research process, from initial idea and design to data collection and analysis. This new edition includes new chapters on collecting and analysing mixed methods data, and additional content on qualitative data analysis. New examples reflect the cultural and global diversity of social research, and extra visual aids and summaries support understanding of key research concepts and stages. The book is accompanied by an online teaching guide, including videos, additional case studies, annotated articles, and critical thinking exercises.
Social Research Methods: Qualitative, Quantitative and Mixed Methods Approaches
by Sigmund GronmoFraming research as the process of asking and answering questions, this book demonstrates how to identify good research questions and how to structure and explore them successfully. Whether you are just beginning your research journey or are a seasoned traveller, it helps you: • Decide what you want to achieve with your research • Know what options you have to explore your goals • Navigate the nuances of different research approaches • Understand the decisions of other researchers • Choose what path best suits your project. Through real-life examples demonstrating different types of research, the book introduces qualitative, quantitative, and mixed methods approaches so you can compare different methods at every stage of the research process, from initial idea and design to data collection and analysis. This new edition includes new chapters on collecting and analysing mixed methods data, and additional content on qualitative data analysis. New examples reflect the cultural and global diversity of social research, and extra visual aids and summaries support understanding of key research concepts and stages. The book is accompanied by an online teaching guide, including videos, additional case studies, annotated articles, and critical thinking exercises.
Social Research Methods: Qualitative, Quantitative and Mixed Methods Approaches
by Sigmund GronmoFraming research as the process of asking and answering questions, this book demonstrates how to identify good research questions and how to structure and explore them successfully. Whether you are just beginning your research journey or are a seasoned traveller, it helps you: • Decide what you want to achieve with your research • Know what options you have to explore your goals • Navigate the nuances of different research approaches • Understand the decisions of other researchers • Choose what path best suits your project. Through real-life examples demonstrating different types of research, the book introduces qualitative, quantitative, and mixed methods approaches so you can compare different methods at every stage of the research process, from initial idea and design to data collection and analysis. This new edition includes new chapters on collecting and analysing mixed methods data, and additional content on qualitative data analysis. New examples reflect the cultural and global diversity of social research, and extra visual aids and summaries support understanding of key research concepts and stages. The book is accompanied by an online teaching guide, including videos, additional case studies, annotated articles, and critical thinking exercises.
Societal Impacts of Artificial Intelligence and Machine Learning (Synthesis Lectures on Computer Science)
by Carlo LipizziThis book goes beyond the current hype of expectations generated by the news on artificial intelligence and machine learning by analyzing realistic expectations for society, its limitations, and possible future scenarios for the use of this technology in our current society. Artificial Intelligence is one of the top topics today and is inflating expectations beyond what the technology can do in the foreseeable future. The future cannot be predicted, but the future of some elements of our society, such as technology, can be estimated. This book merges the modeling of human reasoning with the power of AI technology allowing readers to make more informed decisions about their personal or financial decisions or just being more educated on current technologies. This book presents a model that sketches potential future scenarios based on a discussion of the expectations today, the analysis of the current gap in the literature, and a view of possible futures in terms of technology and use cases. Specifically, this book merges literature on the technology aspects, the sociological impacts, and philosophical aspects.
SOFSEM 2024: 49th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2024, Cochem, Germany, February 19–23, 2024, Proceedings (Lecture Notes in Computer Science #14519)
by Henning Fernau Serge Gaspers Ralf KlasingThis book constitutes the proceedings of the 49th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2024, held in Cochem, Germany, in February 2024. The 33 full papers presented in this book were carefully reviewed and selected from 81 submissions. The book also contains one invited talk in full paper length. They focus on original research and challenges in foundations of computer science including algorithms, AI-based methods, computational complexity, and formal models.
Soft Computing and Its Engineering Applications: 5th International Conference, icSoftComp 2023, Changa, Anand, India, December 7–9, 2023, Revised Selected Papers, Part II (Communications in Computer and Information Science #2031)
by Kanubhai K. Patel Kc Santosh Atul Patel Ashish GhoshThe two-volume proceedings constitutes the refereed proceedings of the 5th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2023, held in Changa, Anand, India, in December 2023. The 42 full papers and 2 short papers included in this book were carefully reviewed and selected from 351 submissions. They are organized in topical sections as follows: Volume number 2020: Theory and Methods; Systems and ApplicationsVolume number 2031: Systems and Applications; Hybrid Techniques.
Soft Computing and Its Engineering Applications: 5th International Conference, icSoftComp 2023, Changa, Anand, India, December 7–9, 2023, Revised Selected Papers, Part I (Communications in Computer and Information Science #2030)
by Kanubhai K. Patel Kc Santosh Atul Patel Ashish GhoshThe two-volume proceedings constitutes the refereed proceedings of the 5th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2023, held in Changa, Anand, India, in December 2023. The 42 full papers and 2 short papers included in this book were carefully reviewed and selected from 351 submissions. They are organized in topical sections as follows: Volume number 2020: Theory and Methods; Systems and ApplicationsVolume number 2031: Systems and Applications; Hybrid Techniques.
Solutions of Fixed Point Problems with Computational Errors (Springer Optimization and Its Applications #210)
by Alexander J. ZaslavskiThe book is devoted to the study of approximate solutions of fixed point problems in the presence of computational errors. It begins with a study of approximate solutions of star-shaped feasibility problems in the presence of perturbations. The goal is to show the convergence of algorithms, which are known as important tools for solving convex feasibility problems and common fixed point problems.The text also presents studies of algorithms based on unions of nonexpansive maps, inconsistent convex feasibility problems, and split common fixed point problems. A number of algorithms are considered for solving convex feasibility problems and common fixed point problems. The book will be of interest for researchers and engineers working in optimization, numerical analysis, and fixed point theory. It also can be useful in preparation courses for graduate students. The main feature of the book which appeals specifically to this audience is the study of the influence of computational errorsfor several important algorithms used for nonconvex feasibility problems.
Solving Optimization Problems with the Heuristic Kalman Algorithm: New Stochastic Methods (Springer Optimization and Its Applications #212)
by Rosario ToscanoThis text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems.The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design.
Solving Ordinary Differential Equations in Python (Simula SpringerBriefs on Computing #15)
by Joakim SundnesThis open access volume explains the foundations of modern solvers for ordinary differential equations (ODEs). Formulating and solving ODEs is an essential part of mathematical modeling and computational science, and numerous solvers are available in commercial and open source software. However, no single ODE solver is the best choice for every single problem, and choosing the right solver requires fundamental insight into how the solvers work. This book will provide exactly that insight, to enable students and researchers to select the right solver for any ODE problem of interest, or implement their own solvers if needed. The presentation is compact and accessible, and focuses on the large and widely used class of solvers known as Runge-Kutta methods. Explicit and implicit methods are motivated and explained, as well as methods for error control and automatic time step selection, and all the solvers are implemented as a class hierarchy in Python.
Space Data Management (Studies in Big Data #141)
by Agostino CortesiThe book presents latest results pertaining to challenges faced by researchers in space data management. There are large number of highly important applications that currently rely upon data from satellites and space missions, ranging from weather prediction to monitoring of environmental pollution, climate change, marine traffic, agriculture, and urban planning, etc. The quality and quantity of space data poses new and specific challenges to the scientific community, covering the entire life cycle of space data management and with a need for an interdisciplinary approach. The chapters written by experts discusses impacts of these challenges, and report activities that can stimulate new research.
Space Information Networks: 7th International Conference, SINC 2023, Wuhan, China, October 12–13, 2023, Revised Selected Papers (Communications in Computer and Information Science #2057)
by Quan YuThis book constitutes revised selected papers from the thoroughly refereed proceedings of the 7th International Conference on Space Information Network, SINC 2023, held in Wuhan, China, during October 12–13, 2023. The 8 full papers and 5 short papers included in this book were carefully reviewed and selected from 73 submissions. The papers present the latest research in the fields of space information networks.
Spatial Linear Models for Environmental Data (Chapman & Hall/CRC Applied Environmental Statistics)
by Dale L. Zimmerman Jay M. Ver HoefMany applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions. Topics covered include: Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c. Ordinary and generalized least squares regression methods and their application to spatial data. Suitable parametric models for the mean and covariance structure of geostatistical and areal data. Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters. Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems. All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.
Spatial Linear Models for Environmental Data (Chapman & Hall/CRC Applied Environmental Statistics)
by Dale L. Zimmerman Jay M. Ver HoefMany applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions. Topics covered include: Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c. Ordinary and generalized least squares regression methods and their application to spatial data. Suitable parametric models for the mean and covariance structure of geostatistical and areal data. Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters. Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems. All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.
Spatial Statistics for Data Science: Theory and Practice with R (Chapman & Hall/CRC Data Science Series)
by Paula MoragaSpatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners. Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
Spatial Statistics for Data Science: Theory and Practice with R (Chapman & Hall/CRC Data Science Series)
by Paula MoragaSpatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners. Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
Spatio-Temporal Models for Ecologists (Chapman & Hall/CRC Applied Environmental Statistics)
by James Thorson Kasper KristensenEcological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists.Key Features: Foundational ecological principles and analyses Thoughtful and thorough ecological examples Analyses conducted using a minimal toolbox and fast computation Code using R and TMB included in the book and available online
Spatio-Temporal Models for Ecologists (Chapman & Hall/CRC Applied Environmental Statistics)
by James Thorson Kasper KristensenEcological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists.Key Features: Foundational ecological principles and analyses Thoughtful and thorough ecological examples Analyses conducted using a minimal toolbox and fast computation Code using R and TMB included in the book and available online
Spectra and Normal Forms (SpringerBriefs in Mathematics)
by Luís Barreira Claudia VallsThis book presents the reader with a streamlined exposition of the notions and results leading to the construction of normal forms and, ultimately, to the construction of smooth conjugacies for the perturbations of tempered exponential dichotomies. These are exponential dichotomies for which the exponential growth rates of the underlying linear dynamics never vanish. In other words, its Lyapunov exponents are all nonzero. The authors consider mostly difference equations, although they also briefly consider the case of differential equations. The content is self-contained and all proofs have been simplified or even rewritten on purpose for the book so that all is as streamlined as possible. Moreover, all chapters are supplemented by detailed notes discussing the origins of the notions and results as well as their proofs, together with the discussion of the proper context, also with references to precursor results and further developments. A useful chapter dependence chart is included in the Preface. The book is aimed at researchers and graduate students who wish to have a sufficiently broad view of the area, without the discussion of accessory material. It can also be used as a basis for graduate courses on spectra, normal forms, and smooth conjugacies.The main components of the exposition are tempered spectra, normal forms, and smooth conjugacies. The first two lie at the core of the theory and have an importance that undoubtedly surpasses the construction of conjugacies. Indeed, the theory is very rich and developed in various directions that are also of interest by themselves. This includes the study of dynamics with discrete and continuous time, of dynamics in finite and infinite-dimensional spaces, as well as of dynamics depending on a parameter. This led the authors to make an exposition not only of tempered spectra and subsequently of normal forms, but also briefly of some important developments in those other directions. Afterwards the discussion continues with the construction of stable and unstable invariant manifolds and, consequently, of smooth conjugacies, while using most of the former material.The notion of tempered spectrum is naturally adapted to the study of nonautonomous dynamics. The reason for this is that any autonomous linear dynamics with a tempered exponential dichotomy has automatically a uniform exponential dichotomy. Most notably, the spectra defined in terms of tempered exponential dichotomies and uniform exponential dichotomies are distinct in general. More precisely, the tempered spectrum may be smaller, which causes that it may lead to less resonances and thus to simpler normal forms. Another important aspect is the need for Lyapunov norms in the study of exponentially decaying perturbations and in the study of parameter-dependent dynamics. Other characteristics are the need for a spectral gap to obtain the regularity of the normal forms on a parameter and the need for a careful control of the small exponential terms in the construction of invariant manifolds and of smooth conjugacies.
Spectral Geometry of Graphs (Operator Theory: Advances and Applications #293)
by Pavel KurasovThis open access book gives a systematic introduction into the spectral theory of differential operators on metric graphs. Main focus is on the fundamental relations between the spectrum and the geometry of the underlying graph.The book has two central themes: the trace formula and inverse problems.The trace formula is relating the spectrum to the set of periodic orbits and is comparable to the celebrated Selberg and Chazarain-Duistermaat-Guillemin-Melrose trace formulas. Unexpectedly this formula allows one to construct non-trivial crystalline measures and Fourier quasicrystals solving one of the long-standing problems in Fourier analysis. The remarkable story of this mathematical odyssey is presented in the first part of the book.To solve the inverse problem for Schrödinger operators on metric graphs the magnetic boundary control method is introduced. Spectral data depending on the magnetic flux allow one to solve the inverse problem in full generality, this means to reconstruct not only the potential on a given graph, but also the underlying graph itself and the vertex conditions.The book provides an excellent example of recent studies where the interplay between different fields like operator theory, algebraic geometry and number theory, leads to unexpected and sound mathematical results. The book is thought as a graduate course book where every chapter is suitable for a separate lecture and includes problems for home studies. Numerous illuminating examples make it easier to understand new concepts and develop the necessary intuition for further studies.