Living Without Mathematical Statistics, Herbert Ruefer
Автор: Riley Название: Mathematical Methods for Physics and Engineering ISBN: 0521679710 ISBN-13(EAN): 9780521679718 Издательство: Cambridge Academ Рейтинг: Цена: 7920.00 р. Наличие на складе: Есть (1 шт.) Описание: This highly acclaimed undergraduate textbook teaches all the mathematics for undergraduate courses in the physical sciences. Containing over 800 exercises, half come with hints and answers and, in a separate manual, complete worked solutions. The remaining exercises are intended for unaided homework; full solutions are available to instructors.
Автор: Thomas A. Garrity Название: All the Math You Missed ISBN: 1009009192 ISBN-13(EAN): 9781009009195 Издательство: Cambridge Academ Рейтинг: Цена: 3960.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second edition of this bestselling book provides an overview of the key topics in undergraduate mathematics, allowing beginning graduate students to fill in any gaps in their knowledge. With numerous examples, exercises and suggestions for further reading, it is a must-have for anyone looking to learn some serious mathematics quickly.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Описание: Featuring recent advances in probability, statistics, and stochastic processes, this new textbook presents Probability and Statistics, and an introduction to Stochastic Processes.
Автор: Sivan Toledo Название: Location Estimation from the Ground Up ISBN: 1611976286 ISBN-13(EAN): 9781611976281 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 8715.00 р. Наличие на складе: Нет в наличии.
Описание: The location of an object can often be determined from indirect measurements using a process called estimation. This book explains the mathematical formulation of location-estimation problems and the statistical properties of these mathematical models. It also presents algorithms that are used to resolve these models to obtain location estimates, including the simplest linear models, nonlinear models (location estimation using satellite navigation systems and estimation of the signal arrival time from those satellites), dynamical systems (estimation of an entire path taken by a vehicle), and models with integer ambiguities (GPS location estimation that is centimeter-level accurate).Location Estimation from the Ground Up clearly presents analytic and algorithmic topics not covered in other books, including simple algorithms for Kalman filtering and smoothing, the solution of separable nonlinear optimization problems, estimation with integer ambiguities, and the implicit-function approach to estimating covariance matrices when the estimator is a minimizer or maximizer. It takes a unified approach to estimation while highlighting the differences between classes of estimation problems. The only book on estimation written for math and computer science students and graduates, it includes problems at the end of each chapter, many with solutions, to help readers deepen their understanding of the material and guide them through small programming projects that apply theory and algorithms to the solution of real-world location-estimation problems.The book’s core audience consists of engineers, including software engineers and algorithm developers, and graduate students who work on location-estimation projects and who need help translating the theory into algorithms, code, and deep understanding of the problem in front of them. Instructors in mathematics, computer science, and engineering may also find the book of interest as a primary or supplementary text for courses in location estimation and navigation.
Автор: Ronald K. Pearson Название: Mining Imperfect Data: With Examples in R and Python ISBN: 161197626X ISBN-13(EAN): 9781611976267 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12164.00 р. Наличие на складе: Нет в наличии.
Описание: It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them.As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python.Mining Imperfect Data: With Examples in R and Python, Second Editionpresents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage);includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them; andprovides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities.
Описание: Collecting a set of classical and emerging methods that otherwise would not be available in a single treatment, Foundations of Computational Imaging: A Model-Based Approach is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging. The book is designed to bring together an eclectic group of researchers with a wide variety of applications and disciplines including applied math, physics, chemistry, optics, and signal processing, to address a collection of problems that can benefit from a common set of methods. Inside, readers will find:Basic techniques of model-based image processing.A comprehensive treatment of Bayesian and regularized image reconstruction methods.An integrated treatment of advanced reconstruction techniques such as majorization, constrained optimization, ADMM, and Plug-and-Play methods for model integration.Foundations of Computational Imaging can be used in courses on Model-Based or Computational Imaging, Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. It is also for researchers or practitioners in medical imaging, scientific imaging, commercial imaging, or industrial imaging.
Автор: Bertrand Iooss, Clementine Prieur, Fabrice Gamboa, Sebastien Da Veiga Название: Basics and Trends in Sensitivity Analysis: Theory and Practice in R ISBN: 1611976685 ISBN-13(EAN): 9781611976687 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11161.00 р. Наличие на складе: Нет в наличии.
Описание: This book provides an overview of global sensitivity analysis methods and algorithms, including their theoretical basis and mathematical properties. The authors use a practical point of view and real case studies as well as numerous examples, and applications of the different approaches are illustrated throughout using R code to explain their usage and usefulness in practice. Basics and Trends in Sensitivity Analysis: Theory and Practice in R covers a lot of material, including theoretical aspects of Sobol’ indices as well as sampling-based formulas, spectral methods, and metamodel-based approaches for estimation purposes; screening techniques devoted to identifying influential and noninfluential inputs; variance-based measures when model inputs are statistically dependent (and several other approaches that go beyond variance-based sensitivity measures); and a case study in R related to a COVID-19 epidemic model where the full workflow of sensitivity analysis combining several techniques is presented.This book is intended for engineers, researchers, and undergraduate students who use complex numerical models and have an interest in sensitivity analysis techniques and is appropriate for anyone with a solid mathematical background in basic statistical and probability theories who develops and uses numerical models in all scientific and engineering domains.
Описание: This book constitutes an up-to-date account of principles, methods, and tools for mathematical and statistical modelling in a wide range of research fields, including medicine, health sciences, biology, environmental science, engineering, physics, chemistry, computation, finance, economics, and social sciences.
Автор: Herbert Ruefer Название: Living Without Mathematical Statistics ISBN: 3030076172 ISBN-13(EAN): 9783030076177 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Поставка под заказ.
Описание: The underlying principles invented and developed by Dr. Genichi Taguchi (1924 - 2012), for the design of experiments or simulation calculations in multi-parameter systems, are today known as Taguchi Method. Due to the great success, it was extended to many other areas.The book explains the basics of this method in as much detail as necessary and as simply and graphically as possible. The author shows how broad the current application spectrum is and for which different tasks it can be used. The application examples range from optimizing a fermentation process in biotechnology to minimizing costs in mechanical production and maintaining and improving competitiveness in industrial production.The processes described are ideally suited to finding reliable and precise solutions for a wide variety of problems relatively quickly. A real competitive advantage not only in research but also for companies that want to remain competitive in international business competition.ContentsPart 1: Analysis of VariablesPart 2: Pattern Recognition and DiagnosisPart 3: PrognosisTarget groupsStudents, scientists, engineers or those responsible for development and products learn to use the Taguchi Method with this book - even without any previous mathematical-statistical knowledge.The authorHerbert Ruefer studied physics and obtained his doctorate at the Technical University Karlsruhe, Germany. After a research stay at IBM, San Jose, California, he taught at the San Marcos National University in Lima, Peru. He then took on research, development, and training tasks in the chemical industry in Germany. During this time, the first personal contacts with Dr. Genichi Taguchi and Dr. Yuin Wu took place. After his active professional life, he dedicated himself to special optical methods for astronomical observations. He also lectures at the Universidad Nacional Mayor de San Marcos which awarded him an honorary doctorate in 2017.
Автор: Bourama Toni; Keith Williamson; Nasser Ghariban; D Название: Bridging Mathematics, Statistics, Engineering and Technology ISBN: 1489995110 ISBN-13(EAN): 9781489995117 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Central Configuration of the N-Body Problem and Spacecraft Orbital Design, Dr. Zhifu Xie.- A Note on Fractional Calculus and some Applications. Dr. Gaston N'Guerekata.- A note on Non-Autonomous Systems of Second-Order Differential Equations, Dr. Toka Diagana.- How the Talmud divides an Estate among Creditors, Dr. Stephen Schecter.- On the non-uniqueness of the decomposition of weighted pseudo-almost periodic functions. Dr. Gaston N'Guerekata.-Note on the almost periodic Beverton-Holt equation. Paul Bezandry.- Piecewise-defined Difference Equations: Open Problem, Dr. Candace Kent.- Mathematics Behind Microstructures, Dr. Daniel Vasiliu.- Tool Support for Efficient Programming of Graphics Processing Units, Dr. Kostadin Damevski.- Association Studies of Racial Disparities in Cancer Survivability, Dr. Lisa Walls and Dr. Weidong Mao.- Perfect Hexagons, Elementary Triangle and the Center of Cubic Curve. -Dr. Raymond Fletcher.
Автор: Yilmaz Название: Mathematical Methods for Engineering Applications ISBN: 3031216997 ISBN-13(EAN): 9783031216992 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This proceedings volume convenes selected, peer-reviewed papers presented at the 3rd International Conference on Mathematics and its Applications in Science and Engineering – ICMASE 2022, which was held on July 4–7, 2022 by the Technical University of Civil Engineering of Bucharest, Romania. Works in this volume cover new developments in applications of mathematics in science and engineering, with emphasis on mathematical and computational modeling of real-world problems. Topics range from the use of differential equations to model mechanical structures to the employ of number theory in the development of information security and cryptography. Educational issues specific to the acquisition of mathematical competencies by engineering and science students at all university levels are also touched on. Researchers and university students are the natural audiences for this book, which can be equally appealing to practitioners seeking up-to-date techniques in mathematical applications to different contexts and disciplines.
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