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Stochastic Relations: Foundations for Markov Transition Systems (Chapman & Hall/CRC Studies in Informatics Series)
by Ernst-Erich DoberkatCollecting information previously scattered throughout the vast literature, including the author's own research, Stochastic Relations: Foundations for Markov Transition Systems develops the theory of stochastic relations as a basis for Markov transition systems.After an introduction to the basic mathematical tools from topology, measure
Stochastic Reliability and Maintenance Modeling
by Toshio Nakagawa Tadashi DohiIn honor of the work of Professor Shunji Osaki, Stochastic Reliability and Maintenance Modeling provides a comprehensive study of the legacy of and ongoing research in stochastic reliability and maintenance modeling. Including associated application areas such as dependable computing, performance evaluation, software engineering, communication engineering, distinguished researchers review and build on the contributions over the last four decades by Professor Shunji Osaki. Fundamental yet significant research results are presented and discussed clearly alongside new ideas and topics on stochastic reliability and maintenance modeling to inspire future research. Across 15 chapters readers gain the knowledge and understanding to apply reliability and maintenance theory to computer and communication systems. Stochastic Reliability and Maintenance Modeling is ideal for graduate students and researchers in reliability engineering, and workers, managers and engineers engaged in computer, maintenance and management works.
Stochastic Risk Analysis and Management
by Boris HarlamovThe author investigates the Cramer –Lundberg model, collecting the most interesting theorems and methods, which estimate probability of default for a company of insurance business. These offer different kinds of approximate values for probability of default on the base of normal and diffusion approach and some special asymptotic.
Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation
by Carl Graham Denis TalayIn various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners' aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems. The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of Itô integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view. The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations.
Stochastic Stability of Differential Equations
by Rafail Khasminskii Grigori Noah MilsteinSince the publication of the first edition of the present volume in 1980, the stochastic stability of differential equations has become a very popular subject of research in mathematics and engineering. To date exact formulas for the Lyapunov exponent, the criteria for the moment and almost sure stability, and for the existence of stationary and periodic solutions of stochastic differential equations have been widely used in the literature. In this updated volume readers will find important new results on the moment Lyapunov exponent, stability index and some other fields, obtained after publication of the first edition, and a significantly expanded bibliography. This volume provides a solid foundation for students in graduate courses in mathematics and its applications. It is also useful for those researchers who would like to learn more about this subject, to start their research in this area or to study the properties of concrete mechanical systems subjected to random perturbations.
Stochastic Stability of Differential Equations in Abstract Spaces (London Mathematical Society Lecture Note Series #453)
by Kai LiuThe stability of stochastic differential equations in abstract, mainly Hilbert, spaces receives a unified treatment in this self-contained book. It covers basic theory as well as computational techniques for handling the stochastic stability of systems from mathematical, physical and biological problems. Its core material is divided into three parts devoted respectively to the stochastic stability of linear systems, non-linear systems, and time-delay systems. The focus is on stability of stochastic dynamical processes affected by white noise, which are described by partial differential equations such as the Navier–Stokes equations. A range of mathematicians and scientists, including those involved in numerical computation, will find this book useful. It is also ideal for engineers working on stochastic systems and their control, and researchers in mathematical physics or biology.
Stochastic Structural Dynamics
by Cho W. ToOne of the first books to provide in-depth and systematic application of finite element methods to the field of stochastic structural dynamicsThe parallel developments of the Finite Element Methods in the 1950's and the engineering applications of stochastic processes in the 1940's provided a combined numerical analysis tool for the studies of dynamics of structures and structural systems under random loadings. In the open literature, there are books on statistical dynamics of structures and books on structural dynamics with chapters dealing with random response analysis. However, a systematic treatment of stochastic structural dynamics applying the finite element methods seems to be lacking. Aimed at advanced and specialist levels, the author presents and illustrates direct integration methods for analyzing the statistics of the response of structures to stochastic loads. The analysis methods are based on structural models represented via the Finite Element Method. In addition to stationary linear problems the text also addresses non-stationary excitation and systems with spatially stochastic property variations. A systematic treatment of stochastic structural dynamics applying the finite element methods Highly illustrated throughout and aimed at advanced and specialist levels, it focuses on computational aspects instead of theory Emphasizes results mainly in the time domain with limited contents in the frequency domain Presents and illustrates direct integration methods for analyzing the statistics of the response of structures to stochastic loads.
Stochastic Structural Dynamics: Progress in Theory and Applications
by T. Ariaratnam G.I. SchuellerThis book contains a series of original contributions in the area of Stochastic Dynamics, which demonstrates the impact of Mike Lin's research and teaching in the area of random vibration and structural dynamics.
Stochastic Systems
by Mircea GrigoriuUncertainty is an inherent feature of both properties of physical systems and the inputs to these systems that needs to be quantified for cost effective and reliable designs. The states of these systems satisfy equations with random entries, referred to as stochastic equations, so that they are random functions of time and/or space. The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents: A clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis Probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciences Practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions Stochastic Systems provides key information for researchers, graduate students, and engineers who are interested in the formulation and solution of stochastic problems encountered in a broad range of disciplines. Numerous examples are used to clarify and illustrate theoretical concepts and methods for solving stochastic equations. The extensive bibliography and index at the end of the book constitute an ideal resource for both theoreticians and practitioners.
Stochastic Teams, Games, and Control under Information Constraints (Systems & Control: Foundations & Applications)
by Tamer Başar Serdar YükselThis monograph presents a mathematically rigorous and accessible treatment of the interaction between information, decision, control, and probability in single-agent and multi-agent systems. The book provides a comprehensive and unified theory of information structures for stochastic control, stochastic teams, stochastic games, and networked control systems.Part I of the text is concerned with a general mathematical theory of information structures for stochastic teams, leading to systematic characterizations and classifications, geometric and topological properties, implications on existence, approximations and relaxations, their comparison, and regularity of optimal solutions in information. Information structures in stochastic games are then considered in Part II, and the dependence of equilibrium solutions and behavior on information is demonstrated. Part III studies information design through information theory in networked control systems – both linear and nonlinear – and discusses optimality and stability criteria. Finally, Part IV introduces information and signaling games under several solution concepts, with applications to prior mismatch, cost mismatch and privacy, reputation games and jamming. This text will be a valuable resource for researchers and graduate students interested in control theory, information theory, statistics, game theory, and applied mathematics. Readers should be familiar with the basics of linear systems theory, stochastic processes, and Markov chains.
Stochastic Thermodynamic Treatment of Thermal Anisotropy (Springer Theses)
by Olga Movilla MiangolarraThis thesis advances our understanding of how thermal anisotropy can be exploited to extract work through a mechanism that is quite distinct from the classical Carnot heat engine. Anisotropy, the presence of thermal or chemical gradients, is ubiquitous in the real world and powers the cascade of processes that sustain life. The thesis quantifies, for the first time, the maximum amount of power and efficiency that a suitable mechanism (a Brownian gyrator) can achieve in such conditions. An important contribution at the center of the thesis is to lay out a geometric framework that brings out the importance of an isoperimetric problem to analyze and quantify optimal operation of thermodynamic engines that harvest energy when simultaneously in contact with several heat baths. Fundamental bounds are derived via isoperimetric inequalities which capture the trade-off between work and dissipation that accrue during thermodynamic cycles. A geometric theory that allows such insights is explained first – the theory of optimal mass transport – followed by rudiments of stochastic thermodynamics that allow for quantification of work and entropy production during finite-time thermodynamic transitions. The thesis further explores entropy production due to heat flowing between heat baths of different temperature through the system dynamics, and concludes with analysis as a proof-of-concept of an autonomous engine that harvests energy from a thermal gradient to continuously produce work in a stable limit cycle operation.
Stochastic Tools in Mathematics and Science
by Alexandre J. Chorin Ole H Hald"Stochastic Tools in Mathematics and Science" covers basic stochastic tools used in physics, chemistry, engineering and the life sciences. The topics covered include conditional expectations, stochastic processes, Brownian motion and its relation to partial differential equations, Langevin equations, the Liouville and Fokker-Planck equations, as well as Markov chain Monte Carlo algorithms, renormalization, basic statistical mechanics, and generalized Langevin equations and the Mori-Zwanzig formalism. The applications include sampling algorithms, data assimilation, prediction from partial data, spectral analysis, and turbulence. The book is based on lecture notes from a class that has attracted graduate and advanced undergraduate students from mathematics and from many other science departments at the University of California, Berkeley. Each chapter is followed by exercises. The book will be useful for scientists and engineers working in a wide range of fields and applications. For this new edition the material has been thoroughly reorganized and updated, and new sections on scaling, sampling, filtering and data assimilation, based on recent research, have been added. There are additional figures and exercises. Review of earlier edition: "This is an excellent concise textbook which can be used for self-study by graduate and advanced undergraduate students and as a recommended textbook for an introductory course on probabilistic tools in science." Mathematical Reviews, 2006
Stochastic Transport in Upper Ocean Dynamics II: STUOD 2022 Workshop, London, UK, September 26–29 (Mathematics of Planet Earth #11)
by Dan Crisan Darryl Holm Bertrand Chapron Etienne Mémin Anna RadomskaThis open access proceedings volume brings selected, peer-reviewed contributions presented at the Third Stochastic Transport in Upper Ocean Dynamics (STUOD) 2022 Workshop, held virtually and in person at the Imperial College London, UK, September 26–29, 2022. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA) and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills and accumulation of plastic in the sea.All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including:Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity;Large scale numerical simulations;Data-based stochastic equations for upper ocean dynamics that quantify simulation error;Stochastic data assimilation to reduce uncertainty.These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation and oceanography.
Stochastic Transport in Upper Ocean Dynamics III: STUOD 2023 Workshop, Plouzané, France, September 25–28 (Mathematics of Planet Earth #13)
by Dan Crisan Darryl D. Holm Bertrand Chapron Etienne Mémin Jane-Lisa CoughlanThis open-access proceedings volume brings selected, peer-reviewed contributions presented at the Fourth Stochastic Transport in Upper Ocean Dynamics (STUOD) 2023 Workshop, held at IFREMER in Plouzané, France, September 25–28, 2023. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA), and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage, and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills, and accumulation of plastic in the sea. All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including: Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity; Large-scale numerical simulations; Data-based stochastic equations for upper ocean dynamics that quantify simulation error; Stochastic data assimilation to reduce uncertainty. These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation, and oceanography.
Stochastic Volatility Modeling (Chapman and Hall/CRC Financial Mathematics Series)
by Lorenzo BergomiPacked with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does c
Stochastic Volatility and Realized Stochastic Volatility Models (SpringerBriefs in Statistics)
by Makoto Takahashi Yasuhiro Omori Toshiaki WatanabeThis treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.
Stochastic and Infinite Dimensional Analysis
by Christopher C. Bernido Maria Victoria Carpio-Bernido Martin Grothaus Tobias Kuna Maria João Oliveira José Luís da SilvaThis volumepresents a collection of papers covering applications from a wide range ofsystems with infinitely many degrees of freedom studied using techniques fromstochastic and infinite dimensional analysis, e. g. Feynman path integrals, thestatistical mechanics of polymer chains, complex networks, and quantum fieldtheory. Systems of infinitely many degrees of freedom create their particularmathematical challenges which have been addressed by different mathematicaltheories, namely in the theories of stochastic processes, Malliavin calculus,and especially white noise analysis. Theseproceedings are inspired by a conference held on the occasion of Prof. LudwigStreit's 75th birthday and celebrate his pioneering and ongoing work in thesefields.
Stochastic versus Deterministic Systems of Differential Equations (Chapman & Hall/CRC Pure and Applied Mathematics)
by G. S. Ladde M. SambandhamThis peerless reference/text unfurls a unified and systematic study of the two types of mathematical models of dynamic processes-stochastic and deterministic-as placed in the context of systems of stochastic differential equations. Using the tools of variational comparison, generalized variation of constants, and probability distribution as its met
Stochasticity in Processes
by Peter SchusterThis book has developed over the past fifteen years from a modern course on stochastic chemical kinetics for graduate students in physics, chemistry and biology. The first part presents a systematic collection of the mathematical background material needed to understand probability, statistics, and stochastic processes as a prerequisite for the increasingly challenging practical applications in chemistry and the life sciences examined in the second part. Recent advances in the development of new techniques and in the resolution of conventional experiments at nano-scales have been tremendous: today molecular spectroscopy can provide insights into processes down to scales at which current theories at the interface of physics, chemistry and the life sciences cannot be successful without a firm grasp of randomness and its sources. Routinely measured data is now sufficiently accurate to allow the direct recording of fluctuations. As a result, the sampling of data and the modeling of relevant processes are doomed to produce artifacts in interpretation unless the observer has a solid background in the mathematics of limited reproducibility. The material covered is presented in a modular approach, allowing more advanced sections to be skipped if the reader is primarily interested in applications. At the same time, most derivations of analytical solutions for the selected examples are provided in full length to guide more advanced readers in their attempts to derive solutions on their own. The book employs uniform notation throughout, and a glossary has been added to define the most important notions discussed.
Stochastics of Environmental and Financial Economics
by Fred Espen Benth Giulia Di NunnoThese Proceedings offer a selection of peer-reviewed research and survey papers by some of the foremost international researchers in the fields of finance, energy, stochastics and risk, who present their latest findings on topical problems. The papers cover the areas of stochastic modeling in energy and financial markets; risk management with environmental factors from a stochastic control perspective; and valuation and hedging of derivatives in markets dominated by renewables, all of which further develop the theory of stochastic analysis and mathematical finance. The papers were presented at the first conference on "Stochastics of Environmental and Financial Economics (SEFE)", being part of the activity in the SEFE research group of the Centre of Advanced Study (CAS) at the Academy of Sciences in Oslo, Norway during the 2014/2015 academic year.
Stochastics, Control and Robotics
by Harish ParthasarathyThis book discusses various problems in stochastic Processes, Control Theory, Electromagnetics, Classical and Quantum Field Theory & Quantum Stochastics. The problems are chosen to motivate the interested reader to learn more about these subjects from other standard sources. Stochastic Process theory is applied to the study of differential equations of mechanics subject to external noise. Some issues in general relativity like Geodesic motion, field theory in curved space time etc. are discussed via isolated problems. The more recent quantum stochastic process theory as formulated by R.L. Hudson and K. R. Parathasarathy is discussed. This provides a non commutative operator theoretic version of stochastic process theory. V.P. Belavkin's approach to quantum filtering based on non demolition measurements and Hudson Parathasarathy calculus has been discussed in detail. Quantum versions of the simple exclusion model in Markov process theory have been included. 3D Robots carring a current density interacting with an external Klein- Gordon or Electromagnetic field has been given some attention. The readers will after going through this book, be ready to carry out independent research in classical and quantum field theory and stochastic processes as applied to practical problems.Note: T&F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.
Stochastik 2: Von der Standardabweichung bis zur Beurteilenden Statistik (Grundstudium Mathematik)
by Michael Barot Juraj HromkovičAufbauend auf dem ersten Band, werden in diesem Buch weiterführende Konzepte der Wahrscheinlichkeitstheorie ausführlich und verständlich diskutiert. Mit vielen exemplarisch durchgerechneten Aufgaben, einer Vielzahl weiterer Problemstellungen und ausführlichen Lösungen bietet es dem Leser die Möglichkeit, die eigenen Fähigkeiten ständig zu erweitern und kritisch zu überprüfen und ein tieferes Verständnis der Materie zu erlangen. Realitätsnahe Anwendungen ermöglichen einen Ausblick in die breite Verwendbarkeit dieser Theorie.Auch in diesem Band wird auf die Entwicklung der Begriffsbildung und der mathematischen Konzepte besonderer Wert gelegt, sodass man ihre Bedeutung bei der Erzeugung wie auch ständige Verbesserung von Forschungsinstrumenten für die Untersuchung unserer Welt erleben kann. Gerichtet ist das Buch an Gymnasiasten, Studienanfänger an Hochschulen, Lehrer und Interessierte, die sich mit diesem Gebiet vertraut machen möchten.
Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls
by Norbert HenzeDieses Lehrbuch liefert einen verständnisorientierten Einstieg in die Stochastik und versetzt Sie in die Lage, kompetent „mitreden“ zu können.Der inhaltliche Umfang deckt den Stoff ab, der in einer einführenden Stochastik-Veranstaltung in einem Bachelor-Studiengang vermittelt werden kann. Mathematiklehrkräfte an Gymnasien, Studierende der Mathematik oder Mathematik-affiner Fächer sowie Quereinsteigende aus Industrie oder Wirtschaft erhalten somit den nötigen Einblick in die faszinierende Welt des Zufalls.Das Buch enthält klar definierte Lernziele, entsprechende Lernzielkontrollen am Ende der Kapitel sowie ein ausführliches Stichwortverzeichnis und eignet sich daher sehr gut zum Selbststudium und als Vorlesungsbegleitung. Mehr als 280 Übungsaufgaben mit Lösungen sowie mehr als 160 per QR-Code verlinkte Videos runden das Lernangebot ab; im YouTube-Kanal „Stochastikclips“ des Autors finden sich weitere Videos, die den Text gut ergänzen.Für die 14. Auflage wurden 265 Flashcards zum Buch ergänzt. Diese sind in der Springer-Nature-Flashcards-App verfügbar und erlauben eine Überprüfung des individuellen Lernerfolgs in Hinblick auf die Lernziele. Im Buch wurden darüber hinaus kleinere Korrekturen und Überarbeitungen vorgenommen.
Stochastik für Informatiker: Eine Einführung in einheitlich strukturierten Lerneinheiten
by Noemi KurtDieses Lehrbuch führt in 16 einheitlich gegliederten Kapiteln in die Wahrscheinlichkeitstheorie und Statistik ein. Dabei sind die Lernziele und benötigten Vorkenntnisse jeweils angegeben und erleichtern in Kombination mit prägnanten Zusammenfassungen die Orientierung je Kapitel. Dank vieler durchgerechneter Beispiele und Übungsaufgaben mit Lösungen kann das Buch gut zum Selbststudium oder als Begleitliteratur zur Vorlesung verwendet werden. Nach einer sorgfältigen Einführung der Grundlagen geben weiterführende Kapitel spannende Ausblicke in Anwendungsbereiche der Stochastik und der stochastischen Modellierung – etwa Markov-Ketten, stochastische Algorithmen, Warteschlangen und Monte-Carlo-Simulationen. Leserinnen und Leser erhalten so ein solides mathematisches Fundament, um die Stochastik im weiteren Studium und in der Praxis auch in komplexen Situationen anwenden zu können. Das Buch richtet sich an Studierende der Informatik und technischer Fachrichtungen ab dem dritten Studiensemester. Dozenten liefert es eine passgenaue Auswahl für eine einsemestrige Vorlesung.
Stochastik in den Ingenieurwissenschaften: Eine Einführung mit R
by Christine Müller Liesa DeneckeDas Buch bietet eine ausführliche Einführung in die Wahrscheinlichkeitsrechnung und Statistik für Ingenieur- und Naturwissenschaftler. Es behandelt die wesentlichen grundlegenden Methoden, die insbesondere im ingenieurwissenschaftlichen Bereich ihre Anwendung finden. Anhand von Beispielen und realen Datensätzen werden die Anwendungen der Methoden verdeutlicht und mit der freien Statistik Software R auch die Gelegenheit gegeben, alle Beispiele direkt nachzuvollziehen und die erlernten Methoden auf andere Datensätze anzuwenden. Dazu wird ebenfalls eine kurze Einführung in R gegeben. Am Ende jedes Abschnitts finden sich Übungsaufgaben mit deren Hilfe die Verfahren geübt werden können. Lösungen zu den Aufgaben werden elektronisch bereitgestellt.