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The New S Language

Meaningful Futures with Robots Designing a New Coexistence

Meaningful Futures with Robots Designing a New Coexistence

Soon robots will leave the factories and make their way into living rooms supermarkets and care facilities. They will cooperate with humans in everyday life taking on more than just practical tasks. How should they communicate with us? Do they need eyes a screen or arms? Should they resemble humans? Or may they enrich social situations precisely because they act so differently from humans? Meaningful Futures with Robots: Designing a New Coexistence provides insight into the opportunities and risks that arise from living with robots in the future anchored in current research projects on everyday robotics. As well as generating ideas for robot developers and designers it also critically discusses existing theories and methods for social robotics from different perspectives - ethical design artistical and technological – and presents new approaches to meaningful human-robot interaction design. Key Features: Provides insights into current research on robots from different disciplinary angles with a particular focus on a value-driven design. Includes contributions from designers psychologists engineers philosophers artists and legal scholars among others. Licence line: Chapters 1 3 12 and 15 of this book are available for free in PDF format as Open Access from the individual product page at www. crcpress. com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4. 0 license. | Meaningful Futures with Robots Designing a New Coexistence

GBP 44.99
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New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes e. g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks international migration global trade global food network arms transfers networks of terrorist groups and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance migration trade etc. ) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github. com/SergSHV/slric. | New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

GBP 48.99
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The Shape of Space

Common Zeros of Polynominals in Several Variables and Higher Dimensional Quadrature

Practical Multivariate Analysis

Measuring Society

Analyzing Baseball Data with R Second Edition

Analyzing Baseball Data with R Second Edition

Analyzing Baseball Data with R Second Edition introduces R to sabermetricians baseball enthusiasts and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics including the Pythagorean formula runs expectancy catcher framing career trajectories simulation of games and seasons patterns of streaky behavior of players and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages including dplyr ggplot2 tidyr purrr and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

GBP 52.99
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Geographic Data Science with Python

Stochastic Modelling for Systems Biology Third Edition

Stochastic Modelling for Systems Biology Third Edition

Since the first edition of Stochastic Modelling for Systems Biology there have been many interesting developments in the use of likelihood-free methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems and the statistical inference chapter has also been extended with new methods including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology Third Edition is now supplemented by an additional software library written in Scala described in a new appendix to the book. New in the Third EditionNew chapter on spatially extended systems covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d along with fast approximations based on the spatial chemical Langevin equationSignificantly expanded chapter on inference for stochastic kinetic models from data covering ABC including ABC-SMCUpdated R package including code relating to all of the new materialNew R package for parsing SBML models into simulatable stochastic Petri net modelsNew open-source software library written in Scala replicating most of the functionality of the R packages in a fast compiled strongly typed functional languageKeeping with the spirit of earlier editions all of the new theory is presented in a very informal and intuitive manner keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

GBP 46.99
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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry academia and government. It introduces the tidyverse suite of R packages including the ggplot2 package for data visualization and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses the book covers traditional introductory statistics topics like confidence intervals hypothesis testing and multiple regression modeling while focusing on visualization throughout. Features: ● Assumes minimal prerequisites notably no prior calculus nor coding experience ● Motivates theory using real-world data including all domestic flights leaving New York City in 2013 the Gapminder project and the data journalism website FiveThirtyEight. com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive. com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics at both the undergraduate and graduate levels.

GBP 66.99
1

Handbook of Statistics in Clinical Oncology

Handbook of Statistics in Clinical Oncology

Many new challenges have arisen in the area of oncology clinical trials. New cancer therapies are often based on cytostatic or targeted agents which pose new challenges in the design and analysis of all phases of trials. The literature on adaptive trial designs and early stopping has been exploding. Inclusion of high-dimensional data and imaging techniques have become common practice and statistical methods on how to analyse such data have been refined in this area. A compilation of statistical topics relevant to these new advances in cancer research this third edition of Handbook of Statistics in Clinical Oncology focuses on the design and analysis of oncology clinical trials and translational research. Addressing the many challenges that have arisen since the publication of its predecessor this third edition covers the newest developments involved in the design and analysis of cancer clinical trials incorporating updates to all four parts: Phase I trials: Updated recommendations regarding the standard 3 + 3 and continual reassessment approaches along with new chapters on phase 0 trials and phase I trial design for targeted agents. Phase II trials: Updates to current experience in single-arm and randomized phase II trial designs. New chapters include phase II designs with multiple strata and phase II/III designs. Phase III trials: Many new chapters include interim analyses and early stopping considerations phase III trial designs for targeted agents and for testing the ability of markers adaptive trial designs cure rate survival models statistical methods of imaging as well as a thorough review of software for the design and analysis of clinical trials. Exploratory and high-dimensional data analyses: All chapters in this part have been thoroughly updated since the last edition. New chapters address methods for analyzing SNP data and for developing a score based on gene expression data. In addition chapters on risk calculators and forensic bioinformatics have been added. Accessible to statisticians and oncologists interested in clinical trial methodology the book is a single-source collection of up-to-date statistical approaches to research in clinical oncology.

GBP 52.99
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Inferential Models Reasoning with Uncertainty

Mathematics and Music Composition Perception and Performance

CRC Standard Mathematical Tables and Formulas

A First Course in Linear Model Theory

A First Course in Linear Model Theory

Thoroughly updated throughout A First Course in Linear Model Theory Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach the authors introduce to students the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models. In addition to adding R functionality this second edition features three new chapters and several sections on new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include linear fixed random and mixed effects models generalized linear models Bayesian and hierarchical linear models model selection multiple comparisons and regularized and robust regression. New to the Second Edition: Coverage of inference for linear models has been expanded into two chapters. Expanded coverage of multiple comparisons random and mixed effects models model selection and missing data. A new chapter on generalized linear models (Chapter 12). A new section on multivariate linear models in Chapter 13 and expanded coverage of the Bayesian linear models and longitudinal models. A new section on regularized regression in Chapter 14. Detailed data illustrations using R. The authors' fresh approach methodical presentation wealth of examples use of R and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models generalized linear models nonlinear models and dynamic models.

GBP 82.99
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Galois Theory

Constrained Optimization In The Calculus Of Variations and Optimal Control Theory

Statistical and Econometric Methods for Transportation Data Analysis

Statistical and Econometric Methods for Transportation Data Analysis

The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods … As a lecturer in both transportation and marketing research I find this book an excellent textbook for advanced undergraduate Master’s and Ph. D. students covering topics from simple descriptive statistics to complex Bayesian models. … It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies covering applications in various aspects of transportation planning engineering safety and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis Third Edition can serve as a textbook for advanced undergraduate Masters and Ph. D. students in transportation-related disciplines including engineering economics urban and regional planning and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.

GBP 69.99
1

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Student Solutions Manual for Gallian's Contemporary Abstract Algebra

Whereas many partial solutions and sketches for the odd-numbered exercises appear in the book the Student Solutions Manual written by the author has comprehensive solutions for all odd-numbered exercises and large number of even-numbered exercises. This Manual also offers many alternative solutions to those appearing in the text. These will provide the student with a better understanding of the material. This is the only available student solutions manual prepared by the author of Contemporary Abstract Algebra Tenth Edition and is designed to supplement that text. Table of Contents Integers and Equivalence Relations0. Preliminaries Groups1. Introduction to Groups 2. Groups 3. Finite Groups; Subgroups 4. Cyclic Groups 5. Permutation Groups 6. Isomorphisms 7. Cosets and Lagrange's Theorem 8. External Direct Products 9. Normal Subgroups and Factor Groups 10. Group Homomorphisms 11. Fundamental Theorem of Finite Abelian Groups Rings12. Introduction to Rings 13. Integral Domains14. Ideals and Factor Rings 15. Ring Homomorphisms 16. Polynomial Rings 17. Factorization of Polynomials 18. Divisibility in Integral Domains FieldsFields19. Extension Fields 20. Algebraic Extensions21. Finite Fields 22. Geometric Constructions Special Topics23. Sylow Theorems 24. Finite Simple Groups 25. Generators and Relations 26. Symmetry Groups 27. Symmetry and Counting 28. Cayley Digraphs of Groups 29. Introduction to Algebraic Coding Theory 30. An Introduction to Galois Theory 31. Cyclotomic Extensions Biography Joseph A. Gallian earned his PhD from Notre Dame. In addition to receiving numerous national awards for his teaching and exposition he has served terms as the Second Vice President and the President of the MAA. He has served on 40 national committees chairing ten of them. He has published over 100 articles and authored six books. Numerous articles about his work have appeared in the national news outlets including the New York Times the Washington Post the Boston Globe and Newsweek among many others. | Student Solutions Manual for Gallian's Contemporary Abstract Algebra

GBP 44.99
1

Monomial Algebras

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem social and infrastructure resilience given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation climate modeling and long-term prediction; approach the problems of increasing frequency of extreme events sea level rise and forest fires as well as economic losses analysis of climate impacts for insurance agriculture fisheries and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling statistics and machine learning including the global circulation models (GCM) and ocean models statistical generalized additive models (GAM) and generalized linear models (GLM) state space and graphical models causality networks Bayesian ensembles a variety of index methods and statistical tests and machine learning methods. The reader will learn about data from various sources including GCM and ocean model outputs satellite observations and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

GBP 54.99
1

Principles of Uncertainty

A Concise Introduction to Statistical Inference

Real Analysis and Foundations

Real Analysis and Foundations

Through four editions this popular textbook attracted a loyal readership and widespread use. Students find the book to be concise accessible and complete. Instructors find the book to be clear authoritative and dependable. The primary goal of this new edition remains the same as in previous editions. It is to make real analysis relevant and accessible to a broad audience of students with diverse backgrounds while also maintaining the integrity of the course. This text aims to be the generational touchstone for the subject and the go-to text for developing young scientists. This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory logic and methods of proof. The more advanced material in the book is made more apparent. This new edition offers a new chapter on metric spaces and their applications. Metric spaces are important in many parts of the mathematical sciences including data mining web searching and classification of images. The author also revised the material on sequences and series adding examples and exercises that compare convergence tests and give additional tests. The text includes rare topics such as wavelets and applications to differential equations. The level of difficulty moves slowly becoming more sophisticated in later chapters. Students have commented on the progression as a favorite aspect of the textbook. The author is perhaps the most prolific expositor of upper division mathematics. With over seventy books in print thousands of students have been taught and learned from his books. | Real Analysis and Foundations

GBP 82.99
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