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Statistical Sensor Fusion, Fredrik Gustafsson


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Цена: 128690.00T
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Автор: Fredrik Gustafsson
Название:  Statistical Sensor Fusion
ISBN: 9789144127248
Издательство: Gazelle Book Services
Классификация:

ISBN-10: 9144127243
Обложка/Формат: Paperback
Страницы: 543
Вес: 0.79 кг.
Дата издания: 08.06.2018
Серия: Computing & IT
Язык: English
Размер: 230 x 155
Читательская аудитория: Further/higher education
Ключевые слова: Computer science
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Поставляется из: Англии
Описание: Sensor fusion deals with merging information from two or more sensors, where the area of statistical signal processing provides a powerful tool­box to attack both theoretical and practical problems. The objective of this book is to explain state of the art theory and algo­rithms in statistical sensor fusion, covering estimation, detection and non­linear filtering theory with applications to localisation, navi­gation and tracking problems. The book starts with a review of the theory on linear and nonlinear estimation, with a focus on sensor network applications. Then, general nonlinear filter theory is surveyed with a particular attention to different variants of the Kalman filter and the particle filter. Complexity and implementation issues are discussed in detail. Simultaneous localisation and mapping (SLAM) is used as a challenging application area of high-dimensional nonlinear filtering problems. The book spans the whole range from mathematical foundations pro­vided in extensive appendices, to real-world problems covered in a part surveying standard sensors, motion models and applications in this field. All models and algorithms are available as object-oriented Matlab code with an extensive data file library, and the examples, which are richly used to illustrate the theory, are supplemented by fully reproducible Matlab code.

Introduction to statistical learning

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
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Цена: 55890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.

An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.


Statistical Rethinking

Автор: McElreath, Richard
Название: Statistical Rethinking
ISBN: 036713991X ISBN-13(EAN): 9780367139919
Издательство: Taylor&Francis
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Цена: 83690.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

Introduction to finite elements in engineering

Автор: Chandrupatla, Tirupathi, Belegundu, Ashok
Название: Introduction to finite elements in engineering
ISBN: 1108841414 ISBN-13(EAN): 9781108841412
Издательство: Cambridge Academ
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Цена: 79190.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Thoroughly updated with improved pedagogy, the fifth edition provides senior undergraduate and graduate students with a clear, comprehensive introduction to the field. Features enhanced coverage of introductory topics, over thirty additional solved problems; downloadable Matlab, Python, C, and Javascript code; and solutions for instructors.

Handbook of Statistical Analysis and Data Mining Applications, 2 ed.

Автор: Robert Nisbet , Gary Miner, Ken Yale
Название: Handbook of Statistical Analysis and Data Mining Applications, 2 ed.
ISBN: 0124166326 ISBN-13(EAN): 9780124166325
Издательство: Elsevier Science
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Цена: 88690.00 T
Наличие на складе: Нет в наличии.
Описание:

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas--from science and engineering, to medicine, academia and commerce.

  • Includes input by practitioners for practitioners
  • Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
  • Contains practical advice from successful real-world implementations
  • Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
  • Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Dynamic Documents with R and knitr, Second Edition

Автор: Xie Y.
Название: Dynamic Documents with R and knitr, Second Edition
ISBN: 1498716962 ISBN-13(EAN): 9781498716963
Издательство: Taylor&Francis
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Цена: 78590.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Quickly and Easily Write Dynamic Documents

Suitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting. Reports range from homework, projects, exams, books, blogs, and web pages to virtually any documents related to statistical graphics, computing, and data analysis. The book covers basic applications for beginners while guiding power users in understanding the extensibility of the knitr package.

New to the Second Edition

  • A new chapter that introduces R Markdown v2
  • Changes that reflect improvements in the knitr package
  • New sections on generating tables, defining custom printing methods for objects in code chunks, the C/Fortran engines, the Stan engine, running engines in a persistent session, and starting a local server to serve dynamic documents

Boost Your Productivity in Statistical Report Writing and Make Your Scientific Computing with R Reproducible

Like its highly praised predecessor, this edition shows you how to improve your efficiency in writing reports. The book takes you from program output to publication-quality reports, helping you fine-tune every aspect of your report.


Data Science and Machine Learning: Mathematical and Statistical Methods

Автор: Kroese, Dirk P. Botev, Zdravko
Название: Data Science and Machine Learning: Mathematical and Statistical Methods
ISBN: 1138492531 ISBN-13(EAN): 9781138492530
Издательство: Taylor&Francis
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Цена: 93910.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The purpose of this book is to provide an accessible, yet comprehensive, account of data science and machine learning. It is intended for anyone interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

Statistical trend analysis of physically unclonable functions :

Автор: Zolfaghari, Behrouz,
Название: Statistical trend analysis of physically unclonable functions :
ISBN: 036775455X ISBN-13(EAN): 9780367754556
Издательство: Taylor&Francis
Рейтинг:
Цена: 51030.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistical Trend Analysis of Physically Unclonable Functions first presents a review on cryptographic hardware and hardware-assisted cryptography. Afterwards, the authors present a combined survey and research work on PUFs using a systematic approach.

Understanding and Applying Basic Statistical Methods Using R

Автор: Wilcox Rand R.
Название: Understanding and Applying Basic Statistical Methods Using R
ISBN: 1119061393 ISBN-13(EAN): 9781119061397
Издательство: Wiley
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Цена: 72810.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians.

Numerical Analysis Using R

Автор: Griffiths
Название: Numerical Analysis Using R
ISBN: 1107115612 ISBN-13(EAN): 9781107115613
Издательство: Cambridge Academ
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Цена: 78150.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the latest numerical solutions to initial value problems and boundary value problems described by ODEs and PDEs. The author offers practical methods for a wide range of problems and illustrates them in the increasingly popular open source language R, allowing integration with more statistical methods.

Data Management Essentials Using SAS and JMP

Автор: Kezik
Название: Data Management Essentials Using SAS and JMP
ISBN: 1107535034 ISBN-13(EAN): 9781107535039
Издательство: Cambridge Academ
Рейтинг:
Цена: 40130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is designed for the first time or occasional SAS user who needs immediate guidance in navigating, exploring, visualizing, cleaning and reporting on data. It teaches the basic SAS skills essential to data management, including practical exercises with solutions. No formal or informal training is required.

Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition

Автор: Nicholas J. Horton , Ken Kleinman
Название: Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition
ISBN: 1482237369 ISBN-13(EAN): 9781482237368
Издательство: Taylor&Francis
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Цена: 78590.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Improve Your Analytical Skills

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

  • The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
  • New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
  • New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
  • New chapter on simulation that includes examples of data generated from complex models and distributions
  • A detailed discussion of the philosophy and use of the knitr and markdown packages for R
  • New packages that extend the functionality of R and facilitate sophisticated analyses
  • Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.


Applied Statistical Methods in Agriculture, Health and Life

Название: Applied Statistical Methods in Agriculture, Health and Life
ISBN: 3319055542 ISBN-13(EAN): 9783319055541
Издательство: Springer
Рейтинг:
Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. It is developed for undergraduate students in agriculture, nursing, biology and biomedical research.


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