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A Graduate Course on Statistical Inference (Springer Texts in Statistics)
by Bing Li G. Jogesh BabuThis textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.
A Graduate Introduction to Numerical Methods: From the Viewpoint of Backward Error Analysis
by Robert M. Corless Nicolas FillionThis book provides an extensive introduction to numerical computing from the viewpoint of backward error analysis. The intended audience includes students and researchers in science, engineering and mathematics. The approach taken is somewhat informal owing to the wide variety of backgrounds of the readers, but the central ideas of backward error and sensitivity (conditioning) are systematically emphasized. The book is divided into four parts: Part I provides the background preliminaries including floating-point arithmetic, polynomials and computer evaluation of functions; Part II covers numerical linear algebra; Part III covers interpolation, the FFT and quadrature; and Part IV covers numerical solutions of differential equations including initial-value problems, boundary-value problems, delay differential equations and a brief chapter on partial differential equations. The book contains detailed illustrations, chapter summaries and a variety of exercises as well some Matlab codes provided online as supplementary material. "I really like the focus on backward error analysis and condition. This is novel in a textbook and a practical approach that will bring welcome attention. " Lawrence F. Shampine A Graduate Introduction to Numerical Methods and Backward Error Analysis" has been selected by Computing Reviews as a notable book in computing in 2013 Computing Reviews Best of 2013 list consists of book and article nominations from reviewers, CR category editors, the editors-in-chief of journals, and others in the computing community.
Graham Priest on Dialetheism and Paraconsistency (Outstanding Contributions to Logic #18)
by Can Başkent Thomas Macaulay FergusonThis book presents the state of the art in the fields of formal logic pioneered by Graham Priest. It includes advanced technical work on the model and proof theories of paraconsistent logic, in contributions from top scholars in the field. Graham Priest’s research has had a considerable influence on the field of philosophical logic, especially with respect to the themes of dialetheism—the thesis that there exist true but inconsistent sentences—and paraconsistency—an account of deduction in which contradictory premises do not entail the truth of arbitrary sentences. Priest’s work has regularly challenged researchers to reappraise many assumptions about rationality, ontology, and truth.This book collects original research by some of the most esteemed scholars working in philosophical logic, whose contributions explore and appraise Priest’s work on logical approaches to problems in philosophy, linguistics, computation, and mathematics. They provide fresh analyses, critiques, and applications of Priest’s work and attest to its continued relevance and topicality. The book also includes Priest’s responses to the contributors, providing a further layer to the development of these themes.
Grammar-Based Feature Generation for Time-Series Prediction
by Anthony Mihirana De Silva Philip H. W. LeongThis book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.
Grammar for Writing (Fifth Course Gr. #10)
by Phyllis Goldenberg Elaine Epstein Carol Domblewski Martin LeeThis book is designed to take the mystery out of grammar and to help you become a better, more confident writer.
Grammatical Inference
by Wojciech WieczorekThis book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development. >
Grandchildhood in Multigenerational Living: Practices, Meanings, Relations
by Adéla SouralováGrandchildhood in Multigenerational Living: Practices, Meanings, Relations is the first book to sociologically analyse grandchild-grandparent relationships from the perspective of grandchildren. Expanding the knowledge about hitherto under-researched grandchildren, this book puts grandchildren’s perspectives in the centre of qualitative analysis focuses. Presenting grandchildhood in its complexity, the author addresses its multiple dimensions from 54 in-depth interviews with grandchildren living in three-generation households with their parents and grandparents. Drawing upon 'family practices', this book conceptionally develops ‘grandchild practices’ as a new approach to see the diversities and similarities, harmonies and tensions, joys and obligations, or, simply put, the daily ambivalences of family relationships. This unique book is an indispensable resource for researchers and students of family studies and sociology of generations who wish to investigate how grandchildren understand, negotiate and make sense of their relationships with grandparents.
Granular Computing in Decision Approximation
by Lech Polkowski Piotr ArtiemjewThis book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k--nearest neighbors and bayesian classifiers. Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook.
The Grapes of Math: How Life Reflects Numbers and Numbers Reflect Life
by Alex BellosFrom triangles, rotations and power laws, to cones, curves and the dreaded calculus, Alex takes you on a journey of mathematical discovery with his signature wit and limitless enthusiasm. He sifts through over 30,000 survey submissions to uncover the world's favourite number, and meets a mathematician who looks for universes in his garage. He attends the World Mathematical Congress in India, and visits the engineer who designed the first roller-coaster loop. Get hooked on math as Alex delves deep into humankind's turbulent relationship with numbers, and reveals how they have shaped the world we live in.
The Grapes of Math
by Alex BellosWho knew that math could be so incredibly interesting? Alex Bellos rescued math from the geeks with Alex's Adventures in Numberland (published here as Here's Looking at Euclid). A quest for the world's most weird and wonderful mathematical phenomena; it won rave reviews and huge sales in his native UK. The Game of Life is again a heady cocktail of history, reportage, and mathematical proofs that will leave you awestruck. The name comes from a math game that replicates how life forms evolve--there's a mindboggling chapter on it--but captures the book's central theme of human interaction with numbers. Alex Bellos this time goes deeper into the mathematical experience. He has sought and found answers to such questions as: What goes on in the brains of mathematicians? Is there any truth in the stereotype of the mad math genius? What is the link between math ability and neural disorders like autism? And what (because Bellos always approaches his subject with good humour) is the math behind wordplay and the structure of jokes? In this continuation of the journey started in Numberland, Alex Bellos has sought out amazing stories from prominent mathematicians, scientists, innovators and businesspeople. He goes deeper than ever, into some terrifying-looking territory--trigonometry, calculus, the square root of minus one--but it is his great gift that he is able to make-even the most daunting concepts a revelatory delight for the non-mathematical reader.
The Grapes of Math
by Greg TangSixteen clever riddles illuminate quick-and-easy tricks to solving math problems. Math puzzles have never been so much fun!Category: Math Skills"How many grapes are on the vine? Counting each takes too much time. Never fear, I have a hunchThere is a match for every bunch!"Greg Tang, a lifelong lover of math, shares the techniques that have helped him solve problems in the most creative ways! Harry Briggs's vibrant & inviting illustrations create a perfect environment for these innovative games. So open your mind-and have fun!"This...clever math book uses rhyming couplets... riddles...visual clues to help the reader find new ways to group numbers for quick counting...A winning addition!" --Kirkus
The Grapes of Math: Mind-Stretching Math Riddles
by Greg Tang Harry BriggsIllustrated riddles introduce strategies for solving a variety of math problems in using visual clues. Each riddle & the rhyming clues that accompany it, can be answered by applying simple math skills like adding, subtracting, and multiplying.
Graph Algebras and Automata (Chapman & Hall/CRC Pure and Applied Mathematics)
by null Andrei KelarevGraph algebras possess the capacity to relate fundamental concepts of computer science, combinatorics, graph theory, operations research, and universal algebra. They are used to identify nontrivial connections across notions, expose conceptual properties, and mediate the application of methods from one area toward questions of the other four. After
Graph Algorithms
by Shimon Even Guy EvenShimon Even's Graph Algorithms, published in 1979, was a seminal introductory book on algorithms read by everyone engaged in the field. This thoroughly revised second edition, with a foreword by Richard M. Karp and notes by Andrew V. Goldberg, continues the exceptional presentation from the first edition and explains algorithms in a formal but simple language with a direct and intuitive presentation. The book begins by covering basic material, including graphs and shortest paths, trees, depth-first-search, and breadth-first search. The main part of the book is devoted to network flows and applications of network flows, and it ends with chapters on planar graphs and testing graph planarity.
Graph Algorithms: Practical Examples in Apache Spark and Neo4j
by Mark Needham Amy E. HodlerDiscover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection.Learn how graph analytics vary from conventional statistical analysisUnderstand how classic graph algorithms work, and how they are appliedGet guidance on which algorithms to use for different types of questionsExplore algorithm examples with working code and sample datasets from Spark and Neo4jSee how connected feature extraction can increase machine learning accuracy and precisionWalk through creating an ML workflow for link prediction combining Neo4j and Spark
Graph-Based Clustering and Data Visualization Algorithms
by Ágnes Vathy-Fogarassy János AbonyiThis work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.
Graph-Based Modelling in Science, Technology and Art (Mechanisms and Machine Science #107)
by Stanisław Zawiślak Jacek RysińskiThis book presents interdisciplinary, cutting-edge and creative applications of graph theory and modeling in science, technology, architecture and art. Topics are divided into three parts: the first one examines mechanical problems related to gears, planetary gears and engineering installations; the second one explores graph-based methods applied to medical analyses as well as biological and chemical modeling; and the third part includes various topics e.g. drama analysis, aiding of design activities and network visualisation. The authors represent several countries in Europe and America, and their contributions show how different, useful and fruitful the utilization of graphs in modelling of engineering systems can be. The book has been designed to serve readers interested in the subject of graph modelling and those with expertise in related areas, as well as members of the worldwide community of graph modelers.
Graph-Based Representation and Reasoning: 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20–22, 2021, Proceedings (Lecture Notes in Computer Science #12879)
by Tanya Braun Marcel Gehrke Tom Hanika Nathalie HernandezThis book constitutes the proceedings of the 26th International Conference on Conceptual Structures, ICCS 2021, held virtually in September 2021.The 12 full papers and 4 short papers presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The papers are organized in the following topical sections: applications of conceptual structures; theory on conceptual structures, and mining conceptual structures.
Graph-Based Representation and Reasoning: 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1–4, 2019, Proceedings (Lecture Notes in Computer Science #11530)
by Dominik Endres Mehwish Alam Diana ŞotropaThis book constitutes the proceedings of the 24th International Conference on Conceptual Structures, ICCS 2019, held in Marburg, Germany, in July 2019. The 14 full papers and 6 short papers presented were carefully reviewed and selected from 29 submissions. The proceedings also include one of the two invited talks. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. ICCS 2019's theme was entitled "Graphs in Human and Machine Cognition."
Graph-Based Social Media Analysis (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Ioannis PitasFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear alge
Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project
by Estelle ScifoSupercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learningPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExtract meaningful information from graph data with Neo4j's latest version 5Use Graph Algorithms into a regular Machine Learning pipeline in PythonLearn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.Book DescriptionNeo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance.Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You'll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you'll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you'll be able to integrate graph algorithms into your ML pipeline.By the end of this book, you'll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.What you will learnUse the Cypher query language to query graph databases such as Neo4jBuild graph datasets from your own data and public knowledge graphsMake graph-specific predictions such as link predictionExplore the latest version of Neo4j to build a graph data science pipelineRun a scikit-learn prediction algorithm with graph dataTrain a predictive embedding algorithm in GDS and manage the model storeWho this book is forIf you're a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you'll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.
Graph Databases: Applications on Social Media Analytics and Smart Cities
by Christos TjortjisWith social media producing such huge amounts of data, the importance of gathering this rich data, often called "the digital gold rush", processing it and retrieving information is vital. This practical book combines various state-of-the-art tools, technologies and techniques to help us understand Social Media Analytics, Data Mining and Graph Databases, and how to better utilize their potential. Graph Databases: Applications on Social Media Analytics and Smart Cities reviews social media analytics with examples using real-world data. It describes data mining tools for optimal information retrieval; how to crawl and mine data from Twitter; and the advantages of Graph Databases. The book is meant for students, academicians, developers and simple general users involved with Data Science and Graph Databases to understand the notions, concepts, techniques, and tools necessary to extract data from social media, which will aid in better information retrieval, management and prediction.
Graph Drawing and Network Visualization
by Emilio Di Giacomo Anna LubiwThis book constitutes the proceedings of the 23rdInternational Symposium on Graph Drawing and Network Visualization, GD 2015,held in Los Angeles, Ca, USA, in September 2015. The 35 full papers presented together with 7 short papersand 8 posters in this volume were carefully reviewed and selected from 77submissions. Graph Drawing is concerned with the geometric representation ofgraphs and constitutes the algorithmic core of Network Visualization. GraphDrawing and Network Visualization are motivated by applications where it iscrucial to visually analyze and interact with relational datasets. Examples ofsuch application areas include social sciences, Internet and Web computing,information systems, computational biology, networking, VLSI circuit design,and software engineering. This year the Steering Committee of GD decided to extendthe name of the conference from the "International Symposium on GraphDrawing" to the "International Symposium on Graph Drawing and NetworkVisualization" in order to better emphasize the dual focus of theconference on combinatorial and algorithmic aspects as well as the design ofnetwork visualization systems and interfaces.
Graph Drawing and Network Visualization
by Yifan Hu Martin NöllenburgThis book constitutes revised selected papers from the 24th International Symposium on Graph Drawing and Network Visualization, GD 2016, held in Athens, Greece, in September 2016. The 45 papers presented in this volume were carefully reviewed and selected from 99 submissions. They were organized in topical sections named: large graphs and clutter avoidance; clustered graphs; planar graphs, layered and tree drawings; visibility representations; beyond planarity; crossing minimization and crossing numbers; topological graph theory; special graph embeddings; dynamic graphs, contest report.
Graph Energy
by Xueliang Li Ivan Gutman Yongtang ShiThis book is about graph energy. The authors have included many of the important results on graph energy, such as the complete solution to the conjecture on maximal energy of unicyclic graphs, the Wagner-Heuberger's result on the energy of trees, the energy of random graphs or the approach to energy using singular values. It contains an extensive coverage of recent results and a gradual development of topics and the inclusion of complete proofs from most of the important recent results in the area. The latter fact makes it a valuable reference for researchers looking to get into the field of graph energy, further stimulating it with occasional inclusion of open problems. The book provides a comprehensive survey of all results and common proof methods obtained in this field with an extensive reference section. The book is aimed mainly towards mathematicians, both researchers and doctoral students, with interest in the field of mathematical chemistry.