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Foundation Mathematics and Statistics provides the reader with a firm understanding of the maths and stats they will need for a computing degree or diploma. The book will give the reader competency in a range of mathematical tools required for technical subjects, and the confidence they will need in the classroom. Explanations of mathematical tools are supported by real world examples to make this subject accessible. Graded exercises enable the reader to practice and revise each topic. Starting with the basics of arithmetic and algebraic manipulation, the book covers everything from exponentials to logarithms. Providing a general grounding in proportions, ratios and percentages, this book will also help readers to understand probability and set theory. Finally, coverage includes the summary and presentation of statistical data and the drawing of histograms.
Book Synopsis Foundation Mathematics and Statistics by : Thomas Bending
Download or read book Foundation Mathematics and Statistics written by Thomas Bending and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundation Mathematics and Statistics provides the reader with a firm understanding of the maths and stats they will need for a computing degree or diploma. The book will give the reader competency in a range of mathematical tools required for technical subjects, and the confidence they will need in the classroom. Explanations of mathematical tools are supported by real world examples to make this subject accessible. Graded exercises enable the reader to practice and revise each topic. Starting with the basics of arithmetic and algebraic manipulation, the book covers everything from exponentials to logarithms. Providing a general grounding in proportions, ratios and percentages, this book will also help readers to understand probability and set theory. Finally, coverage includes the summary and presentation of statistical data and the drawing of histograms.
Foundations of Mathematics and Statistics is a summary of the basic principles of math and statistics for students that are interested in pursuing studies in the mathematical sciences. The first goal is to provide a good foundation of knowledge and ability with the basics of mathematics. This includes logic, sets, number systems, algebra, geometry, trigonometry, and the calculus. Then the remainder of the book deals with the fundamental topics of applied and mathematical statistics, including probability, random variables, expected value, samples, distributions, hypothesis testing, confidence intervals, and an introduction to linear regression and correlation. The book can be used by all students that need a summary of math fundamentals, with a sound introduction to the basics of statistical thinking and methodology. Those that need a good familiarity with math and statistics would find this book a valuable supplemental reading, along with the fair amount of exercises that are included in order to reinforce the important ideas.
Book Synopsis Foundations of Mathematics and Statistics by : Timothy C. Kearns
Download or read book Foundations of Mathematics and Statistics written by Timothy C. Kearns and published by . This book was released on 2019-01-26 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Mathematics and Statistics is a summary of the basic principles of math and statistics for students that are interested in pursuing studies in the mathematical sciences. The first goal is to provide a good foundation of knowledge and ability with the basics of mathematics. This includes logic, sets, number systems, algebra, geometry, trigonometry, and the calculus. Then the remainder of the book deals with the fundamental topics of applied and mathematical statistics, including probability, random variables, expected value, samples, distributions, hypothesis testing, confidence intervals, and an introduction to linear regression and correlation. The book can be used by all students that need a summary of math fundamentals, with a sound introduction to the basics of statistical thinking and methodology. Those that need a good familiarity with math and statistics would find this book a valuable supplemental reading, along with the fair amount of exercises that are included in order to reinforce the important ideas.
Classic analysis of the foundations of statistics and development of personal probability, one of the greatest controversies in modern statistical thought. Revised edition. Calculus, probability, statistics, and Boolean algebra are recommended.
Book Synopsis The Foundations of Statistics by : Leonard J. Savage
Download or read book The Foundations of Statistics written by Leonard J. Savage and published by Courier Corporation. This book was released on 2012-08-29 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classic analysis of the foundations of statistics and development of personal probability, one of the greatest controversies in modern statistical thought. Revised edition. Calculus, probability, statistics, and Boolean algebra are recommended.
Introducing many innovations in content and methods, this book involves the foundations, basic concepts, and fundamental results of probability theory. Geared toward readers seeking a firm basis for study of mathematical statistics or information theory, it also covers the mathematical notions of experiments and independence. 1970 edition.
Book Synopsis Foundations of Probability by : Alfred Renyi
Download or read book Foundations of Probability written by Alfred Renyi and published by Courier Corporation. This book was released on 2007-01-01 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing many innovations in content and methods, this book involves the foundations, basic concepts, and fundamental results of probability theory. Geared toward readers seeking a firm basis for study of mathematical statistics or information theory, it also covers the mathematical notions of experiments and independence. 1970 edition.
Book Synopsis Mathematical Foundations of Quantum Statistics by : Aleksandr I︠A︡kovlevich Khinchin
Download or read book Mathematical Foundations of Quantum Statistics written by Aleksandr I︠A︡kovlevich Khinchin and published by . This book was released on 1960 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
Book Synopsis Mathematical Foundations for Data Analysis by : Jeff M. Phillips
Download or read book Mathematical Foundations for Data Analysis written by Jeff M. Phillips and published by Springer Nature. This book was released on 2021-03-29 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
Phase space, ergodic problems, central limit theorem, dispersion and distribution of sum functions. Chapters include Geometry and Kinematics of the Phase Space; Ergodic Problem; Reduction to the Problem of the Theory of Probability; Application of the Central Limit Theorem; Ideal Monatomic Gas; The Foundation of Thermodynamics; and more.
Book Synopsis Mathematical Foundations of Statistical Mechanics by : Aleksandr I?Akovlevich Khinchin
Download or read book Mathematical Foundations of Statistical Mechanics written by Aleksandr I?Akovlevich Khinchin and published by Courier Corporation. This book was released on 1949-01-01 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phase space, ergodic problems, central limit theorem, dispersion and distribution of sum functions. Chapters include Geometry and Kinematics of the Phase Space; Ergodic Problem; Reduction to the Problem of the Theory of Probability; Application of the Central Limit Theorem; Ideal Monatomic Gas; The Foundation of Thermodynamics; and more.
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
Book Synopsis Mathematical Foundations of Infinite-Dimensional Statistical Models by : Evarist Giné
Download or read book Mathematical Foundations of Infinite-Dimensional Statistical Models written by Evarist Giné and published by Cambridge University Press. This book was released on 2021-03-25 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
Definitive look at modern analysis, with views of applications to statistics, numerical analysis, Fourier series, differential equations, mathematical analysis, and functional analysis. More than 750 exercises; some hints and solutions. 1981 edition.
Book Synopsis Foundations of Mathematical Analysis by : Richard Johnsonbaugh
Download or read book Foundations of Mathematical Analysis written by Richard Johnsonbaugh and published by Courier Corporation. This book was released on 2012-09-11 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Definitive look at modern analysis, with views of applications to statistics, numerical analysis, Fourier series, differential equations, mathematical analysis, and functional analysis. More than 750 exercises; some hints and solutions. 1981 edition.
Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.
Book Synopsis Foundations and Applications of Statistics by : Randall Pruim
Download or read book Foundations and Applications of Statistics written by Randall Pruim and published by American Mathematical Soc.. This book was released on 2018-04-04 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.
