Vores kunder ligger øverst på Google

Google Ads Specialister fra Vestjylland

Vi er 100% dedikerede til Google Annoncering – Vi har mange års erfaring med Google Ads og den bruger vi på at opsætte, optimere & vedligeholde vores fantastiske kunders konti.

100% Specialiseret i Google Ads
Vi har mange års erfaring fra +300 konti
Ingen lange bindinger & evighedskontrakter
Jævnlig opfølgning med hver enkelt kunde
Vi tager din virksomhed seriøst

10 results (0,17127 seconds)

Brand

Merchant

Price (EUR)

Reset filter

Products
From
Shops

Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

Guides You on the Development and Implementation of B–R EvaluationsBenefit–Risk Assessment Methods in Medical Product Development: Bridging Qualitative and Quantitative Assessments provides general guidance and case studies to aid practitioners in selecting specific benefit–risk (B–R) frameworks and quantitative methods. Leading experts from industry regulatory agencies and academia present practical examples lessons learned and best practices that illustrate how to conduct structured B–R assessment in clinical development and regulatory submission. The first section of the book discusses the role of B–R assessments in medicine development and regulation the need for both a common B–R framework and patient input into B–R decisions and future directions. The second section focuses on legislative and regulatory policy initiatives as well as decisions made at the U. S. FDA’s Center for Devices and Radiological Health. The third section examines key elements of B–R evaluations in a product’s life cycle such as uncertainty evaluation and quantification quantifying patient B–R trade-off preferences ways to identify subgroups with the best B–R profiles and data sources used to assist B–R assessment. The fourth section equips practitioners with tools to conduct B–R evaluations including assessment methodologies a quantitative joint modeling and joint evaluation framework and several visualization tools. The final section presents a rich collection of case studies. With top specialists sharing their in-depth knowledge thought-provoking considerations and practical advice this book offers comprehensive coverage of B–R evaluation methods tools and case studies. It gives practitioners a much-needed toolkit to develop and conduct their own B–R evaluations. | Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

GBP 44.99
1

Random Circulant Matrices

Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the properties of the eigenvalues for various types of circulant matrices such as the usual circulant the reverse circulant and the k-circulant when the dimension of the matrices grow and the entries are random. In particular the behavior of the spectral distribution of the spectral radius and of the appropriate point processes are developed systematically using the method of moments and the various powerful normal approximation results. This behavior varies according as the entries are independent are from a linear process and are light- or heavy-tailed. Arup Bose obtained his B. Stat. M. Stat. and Ph. D. degrees from the Indian Statistical Institute. He has been on its faculty at the Theoretical Statistics and Mathematics Unit Kolkata India since 1991. He is a Fellow of the Institute of Mathematical Statistics and of all three national science academies of India. He is a recipient of the S. S. Bhatnagar Prize and the C. R. Rao Award. He is the author of three books: Patterned Random Matrices Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee) and U-Statistics M_m-Estimators and Resampling (with Snigdhansu Chatterjee). Koushik Saha obtained a B. Sc. in Mathematics from Ramakrishna Mission Vidyamandiara Belur and an M. Sc. in Mathematics from Indian Institute of Technology Bombay. He obtained his Ph. D. degree from the Indian Statistical Institute under the supervision of Arup Bose. His thesis on circulant matrices received high praise from the reviewers. He has been on the faculty of the Department of Mathematics Indian Institute of Technology Bombay since 2014. | Random Circulant Matrices

GBP 44.99
1

Design and Analysis of Experiments and Observational Studies using R

Optimization Modelling Using R

Optimization Modelling Using R

This book covers using R for doing optimization a key area of operations research which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R. With the rapid rise of interest in data analytics a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically the book emphasizes creating formulations before going intoimplementation. Algebraic representation allows for clear understanding and generalizationof large applications and writing formulations is necessary to explain and convey the modeling decisions made. Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B. This book: • Provides and explains code so examples are relatively clear and self-contained. • Emphasizes creating algebraic formulations before implementing. • Focuses on application rather than algorithmic details. • Embodies the philosophy of reproducible research. • Uses open-source tools to ensure access to powerful optimization tools. • Promotes open-source: all materials are available on the author’s github repository. • Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R. • Provides code readers can adapt to their own applications. This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds. | Optimization Modelling Using R

GBP 82.99
1

Risks of Artificial Intelligence

Risks of Artificial Intelligence

If the intelligence of artificial systems were to surpass that of humans humanity would face significant risks. The time has come to consider these issues and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI proposes ways to ensure that AI systems will be beneficial to humans and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence followed by a discussion (Armstrong/Sokala/ÓhÉigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity or a useful tool? Ways to assure beneficial outcomes through ‘machine ethics’ and ‘utility functions’ are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose ‘learning’ and ‘empathy’ as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the ‘fast takeoff’ of artificial intelligence and finally Bishop explains why there is no need to worry because computers will remain in a state of ‘artificial stupidity’. Sharing insights from leading thinkers in artificial intelligence this book provides you with an expert-level perspective of what is on the horizon for AI whether it will be a threat for humanity and how we might counteract this threat.

GBP 44.99
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Transition to Advanced Mathematics

Transition to Advanced Mathematics

This unique and contemporary text not only offers an introduction to proofs with a view towards algebra and analysis a standard fare for a transition course but also presents practical skills for upper-level mathematics coursework and exposes undergraduate students to the context and culture of contemporary mathematics. The authors implement the practice recommended by the Committee on the Undergraduate Program in Mathematics (CUPM) curriculum guide that a modern mathematics program should include cognitive goals and offer a broad perspective of the discipline. Part I offers: An introduction to logic and set theory. Proof methods as a vehicle leading to topics useful for analysis topology algebra and probability. Many illustrated examples often drawing on what students already know that minimize conversation about doing proofs. An appendix that provides an annotated rubric with feedback codes for assessing proof writing. Part II presents the context and culture aspects of the transition experience including: 21st century mathematics including the current mathematical culture vocations and careers. History and philosophical issues in mathematics. Approaching reading and learning from journal articles and other primary sources. Mathematical writing and typesetting in LaTeX. Together these Parts provide a complete introduction to modern mathematics both in content and practice. Table of Contents Part I - Introduction to Proofs Logic and Sets Arguments and Proofs Functions Properties of the Integers Counting and Combinatorial Arguments RelationsPart II - Culture History Reading and Writing Mathematical Culture Vocation and Careers History and Philosophy of Mathematics Reading and Researching Mathematics Writing and Presenting Mathematics Appendix A. Rubric for Assessing Proofs Appendix B. Index of Theorems and Definitions from Calculus and Linear Algebra Bibliography Index Biographies Danilo R. Diedrichs is an Associate Professor of Mathematics at Wheaton College in Illinois. Raised and educated in Switzerland he holds a PhD in applied mathematical and computational sciences from the University of Iowa as well as a master’s degree in civil engineering from the Ecole Polytechnique Fédérale in Lausanne Switzerland. His research interests are in dynamical systems modeling applied to biology ecology and epidemiology. Stephen Lovett is a Professor of Mathematics at Wheaton College in Illinois. He holds a PhD in representation theory from Northeastern University. His other books include Abstract Algebra: Structures and Applications (2015) Differential Geometry of Curves and Surfaces with Tom Banchoff (2016) and Differential Geometry of Manifolds (2019). | Transition to Advanced Mathematics

GBP 82.99
1

Financial Mathematics Two Volume Set

Financial Mathematics Two Volume Set

This textbook provides complete coverage of discrete-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field this one presents multiple problem-solving approaches linking related comprehensive techniques for pricing different types of financial derivatives. Key features: In-depth coverage of discrete-time theory and methodology. Numerous fully worked out examples and exercises in every chapter. Mathematically rigorous and consistent yet bridging various basic and more advanced concepts. Judicious balance of financial theory mathematical and computational methods. Guide to Material. This revision contains: Almost 200 pages worth of new material in all chapters. A new chapter on elementary probability theory. An expanded the set of solved problems and additional exercises. Answers to all exercises. This book is a comprehensive self-contained and unified treatment of the main theory and application of mathematical methods behind modern-day financial mathematics. Table of Contents List of Figures and Tables Preface I Introduction to Pricing and Management of Financial Securities 1 Mathematics of Compounding 2 Primer on Pricing Risky Securities 3 Portfolio Management 4 Primer on Derivative Securities II Discrete-Time Modelling 5 Single-Period Arrow–Debreu Models 6 Introduction to Discrete-Time Stochastic Calculus 7 Replication and Pricing in the Binomial Tree Model 8 General Multi-Asset Multi-Period Model Appendices A Elementary Probability Theory B Glossary of Symbols and Abbreviations C Answers and Hints to Exercises References Index Biographies Giuseppe Campolieti is Professor of Mathematics at Wilfrid Laurier University in Waterloo Canada. He has been Natural Sciences and Engineering Research Council postdoctoral research fellow and university research fellow at the University of Toronto. In 1998 he joined the Masters in Mathematical Finance as an instructor and later as an adjunct professor in financial mathematics until 2002. Dr. Campolieti also founded a financial software and consulting company in 1998. He joined Laurier in 2002 as Associate Professor of Mathematics and as SHARCNET Chair in Financial Mathematics. Roman N. Makarov is Associate Professor and Chair of Mathematics at Wilfrid Laurier University. Prior to joining Laurier in 2003 he was an Assistant Professor of Mathematics at Siberian State University of Telecommunications and Informatics and a senior research fellow at the Laboratory of Monte Carlo Methods at the Institute of Computational Mathematics and Mathematical Geophysics in Novosibirsk Russia. | Financial Mathematics Two Volume Set

GBP 130.00
1

3D Animation for the Raw Beginner Using Autodesk Maya 2e

3D Animation for the Raw Beginner Using Autodesk Maya 2e

3D Animation for the Raw Beginner Using Autodesk Maya is a hands-on academic textbook as well as a do-it-yourself training manual for the individual animator. This second edition has been completely rewritten to take into account updates to Autodesk Maya including Autodesk’s renderer Arnold. It contains entirely new examples and tutorial lessons. All 612 images are in full color. The book directs the reader to the parts of Maya that must be mastered in order to create complete 3D projects and thus it simplifies the process of taking on Maya’s vast and intricate interface while giving the reader a firm foundation on which to build future knowledge of Maya. It also presents brief examples of other popular 3D applications and rendering engines. This principles-based yet pragmatic book: Introduces the basic steps of the 3D modeling materials animation lighting and rendering processes. Presents clear and concise tutorials that link key concepts to practical techniques. Includes access to a webpage for the book: https://buzzking. com/AnimationTextbook/AnimationTextbook. html. On this webpage are videos that cover many of the lessons in the book as well as video tutorials that present bonus material not included in the book. Frees instructors from the painstaking task of developing step-by-step examples to present Maya’s complex interface and basic capabilities. Boasts an easy-to-follow tutorial-based learning style ideal for individual study by aspiring animators and do-it yourselfers. Roger Buzz King is a Professor Emeritus at the University of Colorado at Boulder where he teaches 3D Animation for the Computer Science Department and the Alliance for Technology Learning and Society (ATLAS) an institute dedicated to the application of technology to the arts. Buzz is an independent 3D animator who serves on the board of directors of a 3D animation startup. Buzz has a B. A. in Mathematics from Occidental College an M. S. and Ph. D. in Computer Science from the University of Southern California and an M. Div. from the Iliff School of Theology. Key Features Introduces critical aspects of the 3D animation process Presents clear and concise tutorials that link key concepts to practical techniques Includes access to a dedicated Web site http://3dbybuzz. com featuring useful videos lessons and updates Frees instructors from developing step-by-step examples to present Maya’s complex interface and basic Boasts an easy-to-follow hands-on learning style ideal for individual study by aspiring animators and do-ityourselfers | 3D Animation for the Raw Beginner Using Autodesk Maya 2e

GBP 48.99
1

Games Gambling and Probability An Introduction to Mathematics

Games Gambling and Probability An Introduction to Mathematics

Many experiments have shown the human brain generally has very serious problems dealing with probability and chance. A greater understanding of probability can help develop the intuition necessary to approach risk with the ability to make more informed (and better) decisions. The first four chapters offer the standard content for an introductory probability course albeit presented in a much different way and order. The chapters afterward include some discussion of different games different ideas that relate to the law of large numbers and many more mathematical topics not typically seen in such a book. The use of games is meant to make the book (and course) feel like fun! Since many of the early games discussed are casino games the study of those games along with an understanding of the material in later chapters should remind you that gambling is a bad idea; you should think of placing bets in a casino as paying for entertainment. Winning can obviously be a fun reward but should not ever be expected. Changes for the Second Edition: New chapter on Game Theory New chapter on Sports Mathematics The chapter on Blackjack which was Chapter 4 in the first edition appears later in the book. Reorganization has been done to improve the flow of topics and learning. New sections on Arkham Horror Uno and Scrabble have been added. Even more exercises were added! The goal for this textbook is to complement the inquiry-based learning movement. In my mind concepts and ideas will stick with the reader more when they are motivated in an interesting way. Here we use questions about various games (not just casino games) to motivate the mathematics and I would say that the writing emphasizes a just-in-time mathematics approach. Topics are presented mathematically as questions about the games themselves are posed. Table of Contents Preface1. Mathematics and Probability 2. Roulette and Craps: Expected Value 3. Counting: Poker Hands 4. More Dice: Counting and Combinations and Statistics 5. Game Theory: Poker Bluffing and Other Games 6. Probability/Stochastic Matrices: Board Game Movement 7. Sports Mathematics: Probability Meets Athletics 8. Blackjack: Previous Methods Revisited 9. A Mix of Other Games 10. Betting Systems: Can You Beat the System? 11. Potpourri: Assorted Adventures in Probability Appendices Tables Answers and Selected Solutions Bibliography Biography Dr. David G. Taylor is a professor of mathematics and an associate dean for academic affairs at Roanoke College in southwest Virginia. He attended Lebanon Valley College for his B. S. in computer science and mathematics and went to the University of Virginia for his Ph. D. While his graduate school focus was on studying infinite dimensional Lie algebras he started studying the mathematics of various games in order to have a more undergraduate-friendly research agenda. Work done with two Roanoke College students Heather Cook and Jonathan Marino appears in this book! Currently he owns over 100 different board games and enjoys using probability in his decision-making while playing most of those games. In his spare time he enjoys reading cooking coding playing his board games and spending time with his six-year-old dog Lilly. | Games Gambling and Probability An Introduction to Mathematics

GBP 82.99
1