- Table View
- List View
Berechenbarkeit: Berechnungsmodelle und Unentscheidbarkeit (essentials)
by Karl-Heinz ZimmermannIn diesem essential werden wesentliche Konzepte der Berechenbarkeitstheorie erörtert. Zunächst werden unterschiedliche Modelle der Berechenbarkeit eingeführt und ihre semantische Gleichwertigkeit gezeigt. Dieses Resultat steht in Einklang mit der Church-Turing-These, nach der jede intuitiv berechenbare Funktion partiell-rekursiv ist. Neben zentralen Instrumenten der Berechenbarkeit, wie etwa der Gödelisierung von berechenbaren Funktionen und der Existenz universeller berechenbarer Funktionen, stehen unentscheidbare Probleme im Fokus, wie etwa das Halteproblem sowie das Wortproblem für die Term-Ersetzung. Semi-entscheidbare Mengen werden beleuchtet und die zentralen Sätze von Rice und Rice-Shapiro werden skizziert.
Das Hidden-Markov-Modell: Zufallsprozesse mit verborgenen Zuständen und ihre wahrscheinlichkeitstheoretischen Grundlagen (essentials)
by Karl-Heinz ZimmermannIm Mittelpunkt dieses essentials steht eine Einführung in ein bekanntes statistisches Modell, das Hidden-Markov-Modell.Damit können Probleme bewältigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll.Die Anwendungen des Hidden-Markov-Modells liegen hauptsächlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung.In diesem Büchlein werden die beiden zentralen Problemstellungen in HMMs behandelt.Das Problem der Inferenz wird mit dem berühmten Viterbi-Algorithmus gelöst, und das Problem der Parameterschätzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).
So viel Mathe muss sein!: Gut vorbereitet in ein WiMINT-Studium
by Marc Zimmermann Rita Wurth Karin Lunde Matthias Gercken Wolfgang Erben Rolf Dürr Klaus DürrschnabelMithilfe dieses Arbeitsbuchs wird der Leser auf die mathematischen Herausforderungen in einem WiMINT-Studium vorbereitet. Kurze, verständlich formulierte Texte frischen Schulwissen wie logisches Begründen, Bruchrechnen, Differenzialrechnung oder lineare Gleichungssysteme wieder auf. Hierbei helfen eine Vielzahl an Beispielen und Aufgaben mit Lösungen sowie Selbsttests am Anfang jedes Kapitels, mögliche Stolperfallen schon frühzeitig zu identifizieren. Thematisch orientiert sich das Arbeitsbuch am sogenannten cosh-Mindestanforderungskatalog, welcher von Lehrenden aus Schule und Hochschule gemeinsam entwickelt wurde. Dieser hält nach übereinstimmender Meinung vieler deutscher Hochschulen, Dachverbände und Dozenten das für ein WiMINT-Studium notwendige mathematische Vorwissen fest. Neben allgemeinen mathematischen Kompetenzen werden elementare Algebra, Geometrie, Analysis, lineare Algebra und analytische Geometrie abgedeckt.
Einführung in die Mathematische Optimierung
by Uwe T. Zimmermann Rainer E. BurkardMathematische Optimierung spielt aufgrund der verbreiteten Anwendung des Verfahrens und seiner raschen wissenschaftlichen Entwicklung eine wichtige Rolle im Mathematikstudium. In dem Buch führen die Autoren in die Lineare und Konvexe Optimierung ein und vermitteln darauf aufbauend Fragen der Diskreten und Nichtlinearen Optimierung. Vorausgesetzt werden nur Grundkenntnisse der Linearen Algebra und Analysis. Alle Verfahren werden anhand von ökonomischen Beispielen dargestellt, die einzelnen Schritte im Open-Source-Programm Scilab sind dokumentiert.
Partial Truths: How Fractions Distort Our Thinking
by James C. ZimringA fast-food chain once tried to compete with McDonald’s quarter-pounder by introducing a third-pound hamburger—only for it to flop when consumers thought a third pound was less than a quarter pound because three is less than four. Separately, a rash of suicides by teenagers who played Dungeons and Dragons caused a panic in parents and the media. They thought D&D was causing teenage suicides—when in fact teenage D&D players died by suicide at a much lower rate than the national average. Errors of this type can be found from antiquity to the present, from the Peloponnesian War to the COVID-19 pandemic. How and why do we keep falling into these traps?James C. Zimring argues that many of the mistakes that the human mind consistently makes boil down to misperceiving fractions. We see slews of statistics that are essentially fractions, such as percentages, probabilities, frequencies, and rates, and we tend to misinterpret them. Sometimes bad actors manipulate us by cherry-picking data or distorting how information is presented; other times, sloppy communicators inadvertently mislead us. In many cases, we fool ourselves and have only our own minds to blame. Zimring also explores the counterintuitive reason that these flaws might benefit us, demonstrating that individual error can be highly advantageous to problem solving by groups. Blending key scientific research in cognitive psychology with accessible real-life examples, Partial Truths helps readers spot the fallacies lurking in everyday information, from politics to the criminal justice system, from religion to science, from business strategies to New Age culture.
The Meaning of Something: Rethinking the Logic and the Unity of the Ontology (Logic, Argumentation & Reasoning #29)
by Fosca Mariani ZiniThis innovative volume investigates the meaning of ‘something’ in different recent philosophical traditions in order to rethink the logic and the unity of ontology, without forgetting to compare these views to earlier significative accounts in the history of philosophy. In fact, the revival of interest in “something” in the 19th and 20th centuries as well as in contemporary philosophy can easily be accounted for: it affords the possibility for asking the question: what is there? without engaging in predefined speculative assumptions The issue about “something” seems to avoid any naive approach to the question about what there is, so that it is treated in two main contemporary philosophical trends: “material ontology”, which aims at taking “inventory” of what there is, of everything that is; and “formal ontology”, which analyses the structural features of all there is, whatever it is. The volume advances cutting-edge debates on what is the first et the most general item in ontology, that is to say “something”, because the relevant features of the conceptual core of something are: non-nothingness, otherness. Something means that one being is different from others. The relationality belongs to something.: Therefore, the volume advances cutting-edge debates in phenomenology, analytic philosophy, formal and material ontology, traditional metaphysics.
Complex Network Analysis in Python: Recognize - Construct - Visualize - Analyze - Interpret
by Dmitry ZinovievConstruct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.
STEM in the Technopolis: The Power of STEM Education in Regional Technology Policy
by Cliff Zintgraff Sang C. Suh Bruce Kellison Paul E. RestaThis book addresses how forward-thinking local communities are integrating pre-college STEM education, STEM pedagogy, industry clusters, college programs, and local, state and national policies to improve educational experiences, drive local development, gain competitive advantage for the communities, and lead students to rewarding careers. This book consists of three sections: foundational principles, city/regional case studies from across the globe, and state and national context. The authors explore the hypothesis that when pre-college STEM education is integrated with city and regional development, regions can drive a virtuous cycle of education, economic development, and quality of life.Why should pre-college STEM education be included in regional technology policy? When local leaders talk about regional policy, they usually talk about how government, universities and industry should work together. This relationship is important, but what about the hundreds of millions of pre-college students, taught by tens of millions of teachers, supported by hundreds of thousands of volunteers, who deliver STEM education around the world? Leaders in the communities featured in STEM in the Technopolis have recognized the need to prepare students at an early age, and the power of real-world connections in the process. The authors advocate for this approach to be expanded. They describe how STEM pedagogy, priority industry clusters, cross-sector collaboration, and the local incarnations of global development challenges can be made to work together for the good of all citizens in local communities. This book will be of interest to government policymakers, school administrators, industry executives, and non-profit executives. The book will be useful as a reference to teachers, professors, industry professional volunteers, non-profit staff, and program leaders who are developing, running, or teaching in STEM programs or working to improve quality of life in their communities.
The Monte Carlo Simulation Method for System Reliability and Risk Analysis
by Enrico ZioMonte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.
Environmental Risk Modelling in Banking (Routledge International Studies in Money and Banking)
by Magdalena ZiołoEnvironmental risk directly affects the financial stability of banks since they bear the financial consequences of the loss of liquidity of the entities to which they lend and of the financial penalties imposed resulting from the failure to comply with regulations and for actions taken that are harmful to the natural environment. This book explores the impact of environmental risk on the banking sector and analyzes strategies to mitigate this risk with a special emphasis on the role of modelling. It argues that environmental risk modelling allows banks to estimate the patterns and consequences of environmental risk on their operations, and to take measures within the context of asset and liability management to minimize the likelihood of losses. An important role here is played by the environmental risk modelling methodology as well as the software and mathematical and econometric models used. It examines banks’ responses to macroprudential risk, particularly from the point of view of their adaptation strategies; the mechanisms of its spread; risk management and modelling; and sustainable business models. It introduces the basic concepts, definitions, and regulations concerning this type of risk, within the context of its influence on the banking industry. The book is primarily based on a quantitative and qualitative approach and proposes the delivery of a new methodology of environmental risk management and modelling in the banking sector. As such, it will appeal to researchers, scholars, and students of environmental economics, finance and banking, sociology, law, and political sciences.
Multiple View Geometry in Computer Vision
by Andrew Zisserman Richard HartleyA basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Linear Algebra in Data Science (Compact Textbooks in Mathematics)
by Peter Zizler Roberta La HayeThis textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.
Stochastic Process Variation in Deep-Submicron CMOS
by Amir ZjajoOne of the most notable features of nanometer scale CMOS technology is the increasing magnitude of variability of the key device parameters affecting performance of integrated circuits. The growth of variability can be attributed to multiple factors, including the difficulty of manufacturing control, the emergence of new systematic variation-generating mechanisms, and most importantly, the increase in atomic-scale randomness, where device operation must be described as a stochastic process. In addition to wide-sense stationary stochastic device variability and temperature variation, existence of non-stationary stochastic electrical noise associated with fundamental processes in integrated-circuit devices represents an elementary limit on the performance of electronic circuits. In an attempt to address these issues, Stochastic Process Variation in Deep-Submicron CMOS: Circuits and Algorithms offers unique combination of mathematical treatment of random process variation, electrical noise and temperature and necessary circuit realizations for on-chip monitoring and performance calibration. The associated problems are addressed at various abstraction levels, i. e. circuit level, architecture level and system level. It therefore provides a broad view on the various solutions that have to be used and their possible combination in very effective complementary techniques for both analog/mixed-signal and digital circuits. The feasibility of the described algorithms and built-in circuitry has been verified by measurements from the silicon prototypes fabricated in standard 90 nm and 65 nm CMOS technology.
Exploring Modeling with Data and Differential Equations Using R
by John ZobitzExploring Modeling with Data and Differential Equations Using R provides a unique introduction to differential equations with applications to the biological and other natural sciences. Additionally, model parameterization and simulation of stochastic differential equations are explored, providing additional tools for model analysis and evaluation. This unified framework sits "at the intersection" of different mathematical subject areas, data science, statistics, and the natural sciences. The text throughout emphasizes data science workflows using the R statistical software program and the tidyverse constellation of packages. Only knowledge of calculus is needed; the text’s integrated framework is a stepping stone for further advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists. The text will introduce you to: modeling with systems of differential equations and developing analytical, computational, and visual solution techniques. the R programming language, the tidyverse syntax, and developing data science workflows. qualitative techniques to analyze a system of differential equations. data assimilation techniques (simple linear regression, likelihood or cost functions, and Markov Chain, Monte Carlo Parameter Estimation) to parameterize models from data. simulating and evaluating outputs for stochastic differential equation models. An associated R package provides a framework for computation and visualization of results. It can be found here: https://cran.r-project.org/web/packages/demodelr/index.html.
Upside: Profiting from the Profound Demographic Shifts Ahead
by John Zogby Kenneth W. Gronbach M. J. MoyeDemographics not only define who we are, where we live, and how our numbers change, but—for those who can read beyond the raw figures—they open up hidden business opportunities that lie ahead.What will happen when retiring Boomers free up jobs? How will Generation Y alter housing and transportation? Which states will have the most dynamic workforces? Will American manufacturing rebound as Asia’s population boom stalls?Upside puts this powerful yet little-understood science to work finding answers. Demographer Kenneth Gronbach synthesizes reams of data to show how generations impact markets and economies, and how to target promising trends. Lively and full of surprises, the book explains:What each age cohort is likely to buy now and in coming decades How profits dovetail with consumer numbers What sectors are likely to grow or lag How to make sense of the numbers to chart your own path And moreAs waves of people are born and age, fortunes and futures are determined. Whether you’re an investor, marketer executive, or entrepreneur, Upside helps you spot the potential for profits in ever-shifting demographics.
A Finite Element Primer for Beginners: The Basics (SpringerBriefs in Applied Sciences and Technology)
by Tarek I. ZohdiThe purpose of this primer is to provide the basics of the Finite Element Method, primarily illustrated through a classical model problem, linearized elasticity. The topics covered are: (1) Weighted residual methods and Galerkin approximations, (2) A model problem for one-dimensional linear elastostatics, (3) Weak formulations in one dimension, (4) Minimum principles in one dimension, (5) Error estimation in one dimension, (5) Construction of Finite Element basis functions in one dimension, (6) Gaussian Quadrature, (7) Iterative solvers and element by element data structures, (8) A model problem for three-dimensional linear elastostatics, (9) Weak formulations in three dimensions, (10) Basic rules for element construction in three-dimensions, (11) Assembly of the system and solution schemes, (12) Assembly of the system and solution schemes, (13) An introduction to time-dependent problems and (14) A brief introduction to rapid computation based on domain decomposition and basic parallel processing.
A Finite Element Primer for Beginners: The Basics (SpringerBriefs in Applied Sciences and Technology)
by Tarek I. ZohdiThe purpose of this primer is to provide the basics of the Finite Element Method, primarily illustrated through a classical model problem, linearized elasticity. The topics covered are: (1) Weighted residual methods and Galerkin approximations, (2) A model problem for one-dimensional linear elastostatics, (3) Weak formulations in one dimension, (4) Minimum principles in one dimension, (5) Error estimation in one dimension, (5) Construction of Finite Element basis functions in one dimension, (6) Gaussian Quadrature, (7) Iterative solvers and element by element data structures, (8) A model problem for three-dimensional linear elastostatics, (9) Weak formulations in three dimensions, (10) Basic rules for element construction in three-dimensions, (11) Assembly of the system and solution schemes, (12) Assembly of the system and solution schemes, (13) An introduction to time-dependent problems and (14) A brief introduction to rapid computation based on domain decomposition and basic parallel processing.
Dimensional Analysis and Self-Similarity Methods for Engineers and Scientists
by Bahman ZohuriThis ground-breaking reference provides an overview of key concepts in dimensional analysis, and then pushes well beyond traditional applications in fluid mechanics to demonstrate how powerful this tool can be in solving complex problems across many diverse fields. Of particular interest is the book's coverage of dimensional analysis and self-similarity methods in nuclear and energy engineering. Numerous practical examples of dimensional problems are presented throughout, allowing readers to link the book's theoretical explanations and step-by-step mathematical solutions to practical implementations.
Business Resilience System (BRS) (BRS): Real and Near Real Time Analysis and Decision Making System
by Bahman Zohuri Masoud MoghaddamThis book provides a technical approach to a Business Resilience System with its Risk Atom and Processing Data Point based on fuzzy logic and cloud computation in real time. Its purpose and objectives define a clear set of expectations for Organizations and Enterprises so their network system and supply chain are totally resilient and protected against cyber-attacks, manmade threats, and natural disasters. These enterprises include financial, organizational, homeland security, and supply chain operations with multi-point manufacturing across the world. Market shares and marketing advantages are expected to result from the implementation of the system. The collected information and defined objectives form the basis to monitor and analyze the data through cloud computation, and will guarantee the success of their survivability's against any unexpected threats. This book will be useful for advanced undergraduate and graduate students in the field of computer engineering, engineers that work for manufacturing companies, business analysts in retail and e-Commerce, and those working in the defense industry, Information Security, and Information Technology.
Modern Analytic Methods for Computing Scattering Amplitudes: With Application to Two-Loop Five-Particle Processes (Springer Theses)
by Simone ZoiaThis work presents some essential techniques that constitute the modern strategy for computing scattering amplitudes. It begins with an introductory chapter to fill the gap between a standard QFT course and the latest developments in the field. The author then tackles the main bottleneck: the computation of the loop Feynman integrals. The most efficient technique for their computation is the method of the differential equations. This is discussed in detail, with a particular focus on the mathematical aspects involved in the derivation of the differential equations and their solution. Ample space is devoted to the special functions arising from the differential equations, to their analytic properties, and to the mathematical techniques which allow us to handle them systematically. The thesis also addresses the application of these techniques to a cutting-edge problem of importance for the physics programme of the Large Hadron Collider: five-particle amplitudes at two-loop order. It presents the first analytic results for complete two-loop five-particle amplitudes, in supersymmetric theories and QCD. The techniques discussed here open the door to precision phenomenology for processes of phenomenological interest, such as three-photon, three-jet, and di-photon + jet production.
Computational Intelligence-based Optimization Algorithms: From Theory to Practice
by Babak Zolghadr-AsliComputational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming. These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in. Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms.
Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part I (Lecture Notes in Computer Science #11944)
by Albert Zomaya Laurence T. Yang Sheng WenThe two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019, held in Melbourne, Australia, in December 2019. The 73 full and 29 short papers presented were carefully reviewed and selected from 251 submissions. The papers are organized in topical sections on: Parallel and Distributed Architectures, Software Systems and Programming Models, Distributed and Parallel and Network-based Computing, Big Data and its Applications, Distributed and Parallel Algorithms, Applications of Distributed and Parallel Computing, Service Dependability and Security, IoT and CPS Computing, Performance Modelling and Evaluation.
Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part II (Lecture Notes in Computer Science #11945)
by Albert Zomaya Laurence T. Yang Sheng WenThe two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019, held in Melbourne, Australia, in December 2019. The 73 full and 29 short papers presented were carefully reviewed and selected from 251 submissions. The papers are organized in topical sections on: Parallel and Distributed Architectures, Software Systems and Programming Models, Distributed and Parallel and Network-based Computing, Big Data and its Applications, Distributed and Parallel Algorithms, Applications of Distributed and Parallel Computing, Service Dependability and Security, IoT and CPS Computing, Performance Modelling and Evaluation.
A Numerical Primer for the Chemical Engineer, Second Edition
by Edwin ZondervanDesigned as an introduction to numerical methods for students, this book combines mathematical correctness with numerical performance, and concentrates on numerical methods and problem solving. It applies actual numerical solution strategies to formulated process models to help identify and solve chemical engineering problems. Second edition comes with additional chapter on numerical integration and section on boundary value problems in the relevant chapter. Additional material on general modelling principles, mass/energy balances and separate section on DAE’s is also included. Case study section has been extended with additional examples.
The Art and Science of Econometrics (Routledge Studies in Economic Theory, Method and Philosophy)
by Ping ZongToday econometrics has been widely applied in the empirical study of economics. As an empirical science, econometrics uses rigorous mathematical and statistical methods for economic problems. Understanding the methodologies of both econometrics and statistics is a crucial departure for econometrics. The primary focus of this book is to provide an understanding of statistical properties behind econometric methods. Following the introduction in Chapter 1, Chapter 2 provides the methodological review of both econometrics and statistics in different periods since the 1930s. Chapters 3 and 4 explain the underlying theoretical methodologies for estimated equations in the simple regression and multiple regression models and discuss the debates about p-values in particular. This part of the book offers the reader a richer understanding of the methods of statistics behind the methodology of econometrics. Chapters 5–9 of the book are focused on the discussion of regression models using time series data, traditional causal econometric models, and the latest statistical techniques. By concentrating on dynamic structural linear models like state-space models and the Bayesian approach, the book alludes to the fact that this methodological study is not only a science but also an art. This work serves as a handy reference book for anyone interested in econometrics, particularly in relevance to students and academic and business researchers in all quantitative analysis fields.