Presenting innovative modelling approaches to the analysis of fiscal policy and government debt, this book moves beyond previous models that have relied upon the assumption that various age-specific rates and policy variables remain unchanged when it comes to generating government expenditures and tax revenues. As a result of population ageing, current policy settings in many countries are projected to lead to unsustainable levels of public debt; Tax Policy and Uncertainty explores models that allow for feedbacks and uncertainty to combat this. Applicable to any country, the models in the book explore the optimal timing and extent of tax changes in the face of anticipated high future debt. Chapters produce stochastic debt projections, including probability distribution of debt ratios at each point in time. It also offers important analysis of fiscal policy trade-offs as well as providing advice on when and by how much tax rates should be increased. Economics scholars focusing on fiscal policy will appreciate the improved models in this book that allow both for uncertainty and feedback effects arising from responses to increased debt. It will also be helpful to economic policy advisors and economists in government departments.

As the operations of the world become more and more dependent on highly interconnected, massively complex, networked systems of computational devices, the need to develop a mathematical understanding of their properties and behaviours is increasingly pressing. Our approach, described in this monograph, is to combine the compositionality of formal specification -- using techniques from algebra, computation theory, logic, and probability theory -- with the control of level of abstraction afforded by the classical mathematical modelling method.

Interest rate traders have been using the SABR model to price vanilla products for more than a decade. However this model suffers however from a severe limitation: its inability to value exotic products. A term structure model a la LIBOR Market Model (LMM) is often employed to value these more complex derivatives, however the LMM is unable to capture the volatility smile. A joint SABR LIBOR Market Model is the natural evolution towards a consistent pricing of vanilla and exotic products. Knowledge of these models is essential to all aspiring interest rate quants, traders and risk managers, as well an understanding of their failings and alternatives. SABR and SABR Libor Market Models in Practice is an accessible guide to modern interest rate modelling. Rather than covering an array of models which are seldom used in practice, it focuses on the SABR model, the market standard for vanilla products, the LIBOR Market Model, the most commonly used model for exotic products and the extended SABR LIBOR Market Model. The book takes a hands-on approach, demonstrating simply how to implement and work with these models in a market setting. It bridges the gap between the understanding of the models from a conceptual and mathematical perspective and the actual implementation by supplementing the interest rate theory with modelling specific, practical code examples written in Python.

The Springer Handbook of Spacetime is dedicated to the ground-breaking paradigm shifts embodied in the two relativity theories, and describes in detail the profound reshaping of physical sciences they ushered in. It includes in a single volume chapters on foundations, on the underlying mathematics, on physical and astrophysical implications, experimental evidence and cosmological predictions, as well as chapters on efforts to unify general relativity and quantum physics. The Handbook can be used as a desk reference by researchers in a wide variety of fields, not only by specialists in relativity but also by researchers in related areas that either grew out of, or are deeply influenced by, the two relativity theories: cosmology, astronomy and astrophysics, high energy physics, quantum field theory, mathematics, and philosophy of science. It should also serve as a valuable resource for graduate students and young researchers entering these areas, and for instructors who teach courses on these subjects.

The Handbook is divided into six parts. Part A: Introduction to Spacetime Structure. Part B: Foundational Issues. Part C: Spacetime Structure and Mathematics. Part D: Confronting Relativity theories with observations. Part E: General relativity and the universe. Part F: Spacetime beyond Einstein.

What is the origin of game preferences and payoffs, how are they aggregated and what are the implications of interdependent preferences? What is the importance of information for building game models? How can game models be used to analyse empirical cases? At the cutting edge of current modelling in international relations using non-cooperative game theory, this collection of original contributions from political scientists and economists explores some of the fundamental assumptions of game theory modelling. It includes a theory of game payoff formation, a theory of preference aggregation, thorough discussions of the effects of interdependence between preferences upon various game structures, in-depth analyses of the impact of incomplete information upon dynamic games of negotiation, and a study using differential games. Numerous illustrations, case studies and comparative case studies show the relevance of the theoretical debate. The chapters are organised to allow readers with a limited knowledge of game theory to develop their understanding of the fundamental issues. Containing theoretical discussion of the basic game theory assumptions - as well as means of going beyond them - Game Theory and International Relations will be welcomed by all those interested in the empirical application of game theory models in international relations.

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB (R), this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material - including lecture videos per section, homeworks, data, and code in MATLAB (R), Python, Julia, and R - available on databookuw.com.

Introduction to Traveling Waves is an invitation to research focused on traveling waves for undergraduate and masters level students. Traveling waves are not typically covered in the undergraduate curriculum, and topics related to traveling waves are usually only covered in research papers, except for a few texts designed for students. This book includes techniques that are not covered in those texts. Through their experience involving undergraduate and graduate students in a research topic related to traveling waves, the authors found that the main difficulty is to provide reading materials that contain the background information sufficient to start a research project without an expectation of an extensive list of prerequisites beyond regular undergraduate coursework. This book meets that need and serves as an entry point into research topics about the existence and stability of traveling waves. Features Self-contained, step-by-step introduction to nonlinear waves written assuming minimal prerequisites, such as an undergraduate course on linear algebra and differential equations. Suitable as a textbook for a special topics course, or as supplementary reading for courses on modeling. Contains numerous examples to support the theoretical material. Supplementary MATLAB codes available via GitHub.

Although many archaeologists have a good understanding of the basics in computer science, statistics, geostatistics, modeling, and data mining, more literature is needed about the advanced analysis in these areas. This book aids archaeologists in learning more advanced tools and methods while also helping mathematicians, statisticians, and computer scientists with no previous knowledge of the field realize the potential of the methods in archaeological experiments.

Builds a market simulator to back test trading algorithms Implements closed-form strategies that optimize trading signals Measures liquidity risk and stress test portfolios for fire sales Analyze algorithms’ performance controlling for common trading biases Estimates price impact models using the public trading tape

Features Includes multiple case studies. Suitable for scientists and professionals working on methods for mosquito control. Provides a much-needed focal point for interdisciplinary discussion.

Discusses effluent discharges into various ambient waters and predictive tools for design and regulatory purposes. Emphasis placed on numerical modeling and simulations, rather than general examples. Provides real technical solutions and tools for minimizing the impact on coasts and other water bodies. Covers the fundamentals in predicting the mixing of effluents resulting from desalination plants. Includes an introduction to OpenFOAM and its applications.

Amid recent interest in Clifford algebra for dual quaternions as a more suitable method for Computer Graphics than standard matrix algebra, this book presents dual quaternions and their associated Clifford algebras in a new light, accessible to and geared towards the Computer Graphics community. Collating all the associated formulas and theorems in one place, this book provides an extensive and rigorous treatment of dual quaternions, as well as showing how two models of Clifford algebras emerge naturally from the theory of dual quaternions. Each chapter comes complete with a set of exercises to help readers sharpen and practice their knowledge. This book is accessible to anyone with a basic knowledge of quaternion algebra and is of particular use to forward-thinking members of the Computer Graphics community. .

Autonomous Positioning of Piezoactuated Mechanism for Biological Cell Puncture gives a systematic and almost selfcontained description of the many facets of advanced design, optimization, modeling, system identification and advanced control techniques for positioning of cell puncture mechanism with piezoelectric actuator in micro/nanorobotics systems. To achieve biomedical applications, reliability design, modeling, and precision control are vitally important for developing engineering systems. With the advances in mechanical design, dynamic modeling, system identification, and control techniques, exploiting them for the benefit of reliability design, precision control, and quick actuation of micro/nanomanipulation systems to expand the robot's applications at the micro and nanoscales, especially for biomedical applications. This book unifies existing and emerging techniques concerning advanced design, modeling and advanced control methodologies in micropuncture of biological cells using piezoelectric actuator with their practical biomedical applications. The book is an essential resource for researchers within robotics, mechatronics, biomedical engineering, and automatic control society, including both academic and industrial parts. KEY FEATURES * Provides a series of latest results in, including but not limited to, design, modeling, and control of micro/nanomanipulation systems utilizing piezoelectric actuator * Gives recent advances of theory, technological aspects, and applications of advanced modeling, control, and actuation methodologies in cell engineering applications * Presents simulation and experimental results to reflect the micro/nano manipulation practice, and also validate the performances of the developed design, analysis, and synthesis approaches

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.

Optimization is presented in most multivariable calculus courses as an application of the gradient, and while this treatment makes sense for a calculus course, there is much more to the theory of optimization. Optimization problems are generated constantly, and the theory of optimization has grown and developed in response to the challenges presented by these problems. This textbook aims to show readers how optimization is done in practice and help them to develop an appreciation for the richness of the theory behind the practice. Exercises, problems (including modeling and computational problems), and implementations are incorporated throughout the text to help students learn by doing. Python notes are inserted strategically to help readers complete computational problems and implementations. The Basics of Practical Optimization, Second Edition is intended for undergraduates who have completed multivariable calculus, as well as anyone interested in optimization. The book is appropriate for a course that complements or replaces a standard linear programming course.

Composites have been studied for more than 150 years, and interest in their properties has been growing. This classic volume provides the foundations for understanding a broad range of composite properties, including electrical, magnetic, electromagnetic, elastic and viscoelastic, piezoelectric, thermal, fluid flow through porous materials, thermoelectric, pyroelectric, magnetoelectric, and conduction in the presence of a magnetic field (Hall effect). Exact solutions of the PDEs in model geometries provide one avenue of understanding composites; other avenues include microstructure-independent exact relations satisfied by effective moduli, for which the general theory is reviewed; approximation formulae for effective moduli; and series expansions for the fields and effective moduli that are the basis of numerical methods for computing these fields and moduli. The range of properties that composites can exhibit can be explored either through the model geometries or through microstructure-independent bounds on the properties. These bounds are obtained through variational principles, analytic methods, and Hilbert space approaches. Most interesting is when the properties of the composite are unlike those of the constituent materials, and there has been an explosion of interest in such composites, now known as metamaterials. The Theory of Composites surveys these aspects, among others, and complements the new body of literature that has emerged since the book was written. It remains relevant today by providing historical background, a compendium of numerous results, and through elucidating many of the tools still used today in the analysis of composite properties. This book is intended for applied mathematicians, physicists, and electrical and mechanical engineers. It will also be of interest to graduate students.

Many scientific and real-world problems that occur in science, engineering, and medicine can be represented in differential equations. There is a vital role for differential equations in studying the behavior of different types of real-world problems. Thus, it becomes crucial to know the existence uniqueness properties of differential equations and various methods of finding differential equation solutions in explicit form. It is also essential to know different kinds of differential equations in terms of eigenvalues, termed eigenvalue problems, and some special functions used in finding the solution to differential equations. The study of nonlinear problems also plays a significant role in different real-world situations. There is a necessity to know the behavior of solutions of nonlinear differential equations. Still, there are very few forms of differential equations whose solution can be found in explicit form. For the differential equations whose solutions cannot be found in explicit form, one has to study the properties of solutions of the given differential equation to guess an approximate solution of it. This book aims to introduce all the necessary topics of differential equations in one book so that laymen can easily understand the subject and apply it in their research areas. The novel approach used in this book is that I have introduced different analytical methods for finding the solution of differential equations with sufficient theorems, corollaries, and examples, and the geometrical interpretations in each topic. This textbook is intended to study the theory and methods of finding the explicit solutions to differential equations, wherever possible, and in the absence of finding explicit solutions. It is intended to study the properties of solutions to the given differential equations. This book is based on syllabi of the theory of differential equations prescribed for postgraduate students of mathematics and applied mathematics in different institutions and universities of India and abroad. This book will be helpful for competitive examinations as well.

This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis". This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text. Volume II is composed of the last two parts. Part III examines reliability and availability modeling by covering a set of fundamental notions, definitions, redundancy procedures, and modeling methods such as Reliability Block Diagrams (RBD) and Fault Trees (FT) with the respective evaluation methods, adopts Markov chains, Stochastic Petri nets and even hierarchical and heterogeneous modeling to represent more complex systems. Part IV discusses performance measurements and reliability data analysis. It first depicts some basic measuring mechanisms applied in computer systems, then discusses workload generation. After, we examine failure monitoring and fault injection, and finally, we discuss a set of techniques for reliability and maintainability data analysis.

This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

Includes a balanced coverage of modeling as well as applications of layout, materials handling, and warehousing Presents automated materials handling and warehousing along with queuing, queuing network, and basic simulation modeling Introduces new material on supply chain designing and management, aggregate planning and stochastic inventory control, transportation, and logistics/distribution Provides Layout-iQ software and data files from the authors own website Offers a solutions manual and PowerPoint slides for qualified textbook adoption

Double Sequence Spaces and Four-Dimensional Matrices provides readers with a clear introduction to the spaces of double sequences and series, as well as their properties. The book then goes beyond this to investigate paranormed double sequence spaces and their algebraic and topological properties, triangle matrices and their domains in certain spaces of double sequences, dual spaces of double sequence spaces, and matrix transformations between double sequence spaces and related topics. Each chapter contains a conclusion section highlighting the importance of results and pointing out possible new ideas that can be studied further. Features Suitable for students at graduate or post-graduate level and researchers Investigates different types of summable spaces and computes their duals Characterizes several four-dimensional matrix classes transforming one summable space into other Discusses several algebraic and topological properties of new sequence spaces generated by the domain of triangles.

The field and topic of optimization is not only a very hot topic now, it is morphing into new approaches. Presents a very contemporary approach. Appeal to mathematicians, yet will also find use in computer science and engineering, especially in operations research. Practical approach presents a framework to be used by students and professionals alike to tackle models needed for various applications and solutions.

Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master's programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432.