Mathematics Statistics Computer Science
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Recent Advances In Mathematics Statistics And Computer Science 2015 International Conference
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Author : Arun Kumar Sinha
language : en
Publisher: World Scientific
Release Date : 2016-06-09
Recent Advances In Mathematics Statistics And Computer Science 2015 International Conference written by Arun Kumar Sinha and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-09 with Mathematics categories.
This unique volume presents the scientific achievements, significant discoveries and pioneering contributions of various academicians, industrialist and research scholars. The book is an essential source of reference and provides a comprehensive overview of the author's work in the field of mathematics, statistics and computer science.
Probability And Statistics For Computer Scientists
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Author : Michael Baron
language : en
Publisher: CRC Press
Release Date : 2018-11-14
Probability And Statistics For Computer Scientists written by Michael Baron and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-14 with Mathematics categories.
In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions
Algorithms For Data Science
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Author : Brian Steele
language : en
Publisher: Springer
Release Date : 2016-12-25
Algorithms For Data Science written by Brian Steele and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-25 with Computers categories.
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Basic Elements Of Computational Statistics
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Author : Wolfgang Karl Härdle
language : en
Publisher: Springer
Release Date : 2017-09-29
Basic Elements Of Computational Statistics written by Wolfgang Karl Härdle and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-29 with Computers categories.
This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical, statistical as well as programming problems in computational statistics and contains a wide variety of practical examples. In addition to the numerous R sniplets presented in the text, all computer programs (quantlets) and data sets to the book are available on GitHub and referred to in the book. This enables the reader to fully reproduce as well as modify and adjust all examples to their needs. The book is intended for advanced undergraduate and first-year graduate students as well as for data analysts new to the job who would like a tour of the various statistical tools in a data analysis workshop. The experienced reader with a good knowledge of statistics and programming might skip some sections on univariate models and enjoy the various ma thematical roots of multivariate techniques. The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding Data-Driven Documents-based visualization allows readers to reproduce the tables, pictures and calculations inside this Springer book.
Computer Intensive Methods In Statistics
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Author : Silvelyn Zwanzig
language : en
Publisher: CRC Press
Release Date : 2019-12-11
Computer Intensive Methods In Statistics written by Silvelyn Zwanzig and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-11 with Business & Economics categories.
This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.
Probability And Statistics For Computer Science
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Author : James L. Johnson
language : en
Publisher: John Wiley & Sons
Release Date : 2003-07-22
Probability And Statistics For Computer Science written by James L. Johnson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-22 with Mathematics categories.
Comprehensive and thorough development of both probability and statistics for serious computer scientists; goal-oriented: "to present the mathematical analysis underlying probability results" Special emphases on simulation and discrete decision theory Mathematically-rich, but self-contained text, at a gentle pace Review of calculus and linear algebra in an appendix Mathematical interludes (in each chapter) which examine mathematical techniques in the context of probabilistic or statistical importance Numerous section exercises, summaries, historical notes, and Further Readings for reinforcement of content
Mathematics Statistics Computer Science
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Author : Careers Research and Advisory Centre (Cambridge, England)
language : en
Publisher: Trotman Education
Release Date : 2007-04-15
Mathematics Statistics Computer Science written by Careers Research and Advisory Centre (Cambridge, England) and has been published by Trotman Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-15 with Computer science categories.
Popular among university applicants and their advisers alike, these guides presents a wide range of information on a specific degree discipline, laid out in tabular format enabling at-a-glance course comparison.
Statistical Computing
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Author : WIlliam J. Kennedy
language : en
Publisher: Routledge
Release Date : 2021-06-23
Statistical Computing written by WIlliam J. Kennedy and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-23 with Computers categories.
In this book the authors have assembled the "best techniques from a great variety of sources, establishing a benchmark for the field of statistical computing." ---Mathematics of Computation ." The text is highly readable and well illustrated with examples. The reader who intends to take a hand in designing his own regression and multivariate packages will find a storehouse of information and a valuable resource in the field of statistical computing.
Probability And Statistics
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Author : J. Susan Milton
language : en
Publisher: McGraw-Hill Europe
Release Date : 2012-11
Probability And Statistics written by J. Susan Milton and has been published by McGraw-Hill Europe this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11 with Electronic data processing categories.
Helps students to understand statistical methods and reasoning as well as practice in using them. This book includes examples and exercises that are specially chosen for those looking for careers in the engineering and computing sciences. It is intended as a first course in probability and applied statistics for students.
Current Problems In Applied Mathematics And Computer Science And Systems
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Author : Anatoly Alikhanov
language : en
Publisher: Springer Nature
Release Date : 2023-06-05
Current Problems In Applied Mathematics And Computer Science And Systems written by Anatoly Alikhanov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-05 with Technology & Engineering categories.
This book is based on the best papers accepted for presentation during the International Conference on Actual Problems of Applied Mathematics and Computer Systems (APAMCS-2022), Russia. The book includes research materials on modern mathematical problems, solutions in the field of scientific computing, data analysis and modular computing. The scope of numerical methods in scientific computing presents original research, including mathematical models and software implementations, related to the following topics: numerical methods in scientific computing; solving optimization problems; methods for approximating functions, etc. The studies in data analysis and modular computing include contributions in the field of deep learning, neural networks, mathematical statistics, machine learning methods, residue number system and artificial intelligence. Finally, the book gives insights into the fundamental problems in mathematics education. The book intends for readership specializing in the field of scientific computing, parallel computing, computer technology, machine learning, information security and mathematical education.