Download or read book Algebraic Methods in Statistics and Probability written by Marlos A. G. Viana and published by American Mathematical Soc.. This book was released on 2001 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 23 papers report recent developments in using the technique to help clarify the relationship between phenomena and data in a number of natural and social sciences. Among the topics are a coordinate-free approach to multivariate exponential families, some rank-based hypothesis tests for covariance structure and conditional independence, deconvolution density estimation on compact Lie groups, random walks on regular languages and algebraic systems of generating functions, and the extendibility of statistical models. There is no index. c. Book News Inc.

Download or read book Probability and Statistics written by John Tabak and published by Infobase Publishing. This book was released on 2014-05-14 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a survey of the history and evolution of the branch of mathematics that focuses on probability and statistics, including useful applications and notable mathematicians in this area.

Download or read book Simplified Statistics written by Kingsley Augustine and published by Independently Published. This book was released on 2018-03-10 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a statistics book which is suitable for students in high schools or secondary schools and colleges. It will also serve as a useful tool for students who are preparing for entrance examinations into colleges, universities and other higher institutions of learning. Students in the higher institutions taking 'lighter' courses in statistics and mathematics will also find this book useful, especially when there is need for improved statistical foundation for such students.The step by step explanations presented in the worked examples are easy to study and learn since care was taken to sufficiently explain salient points and mathematical ideas. Efforts have been made to achieve a complete and simplified explanation of every example given in this maths and statistics eBook. Many worked examples have been included in each topic in order to fully cover every complexity the topic might contain.Numerous exercises at the end of each chapter are intended to test students' understanding of the topic. Therefore students are thus presented with an effective means of self-assessment whereby they can determine their individual strengths and revision needs.The topics covered in this book include, collection and tabulation of data, mean median and mode of grouped data, mean median and mode of ungrouped data, mean deviation, variance and standard deviation, quartiles and percentiles by interpolation method.Readers with statistical mindsets will find these topics well simplified, thereby making mathematics more interesting.This e-book is designed to work with pinch-and-zoom on your device. This allows scrolling while zoomed in. You can use device navigation to access the table of contents and switch between chapters. In order to look inside this book, you can simply preview it by sending a sample to your devices.A constructive review of this statistics textbook will be highly appreciated from buyers so as to give ideas to others who intend to purchase a copy of this eBook, and also to be a form of advice for the author when revising the book.From the author of "Simplified Mathematics" and "Simplified Physics Calculations".

Download or read book Probability and Statistics written by Michael J. Evans and published by Macmillan. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.

Download or read book Basic Concepts of Probability and Statistics written by Joseph Lawson Hodges and published by . This book was released on 1966 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Probability and Statistics for Data Science written by Norman Matloff and published by CRC Press. This book was released on 2019-06-21 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

Download or read book Algebraic Statistics written by Seth Sullivant and published by American Mathematical Society. This book was released on 2023-11-17 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algebraic statistics uses tools from algebraic geometry, commutative algebra, combinatorics, and their computational sides to address problems in statistics and its applications. The starting point for this connection is the observation that many statistical models are semialgebraic sets. The algebra/statistics connection is now over twenty years old, and this book presents the first broad introductory treatment of the subject. Along with background material in probability, algebra, and statistics, this book covers a range of topics in algebraic statistics including algebraic exponential families, likelihood inference, Fisher's exact test, bounds on entries of contingency tables, design of experiments, identifiability of hidden variable models, phylogenetic models, and model selection. With numerous examples, references, and over 150 exercises, this book is suitable for both classroom use and independent study.

Download or read book Linear Algebra and Matrix Analysis for Statistics written by Sudipto Banerjee and published by CRC Press. This book was released on 2014-06-06 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction. The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.

Download or read book Algebraic Statistics written by Giovanni Pistone and published by CRC Press. This book was released on 2000-12-21 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by pioneers in this exciting new field, Algebraic Statistics introduces the application of polynomial algebra to experimental design, discrete probability, and statistics. It begins with an introduction to Grobner bases and a thorough description of their applications to experimental design. A special chapter covers the binary case

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book Probability and Mathematical Statistics Theory Applications and Practice in R written by Mary C. Meyer and published by SIAM. This book was released on 2019-06-24 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the theory of probability and mathematical statistics with the goal of analyzing real-world data. Throughout the text, the R package is used to compute probabilities, check analytically computed answers, simulate probability distributions, illustrate answers with appropriate graphics, and help students develop intuition surrounding probability and statistics. Examples, demonstrations, and exercises in the R programming language serve to reinforce ideas and facilitate understanding and confidence. The book’s Chapter Highlights provide a summary of key concepts, while the examples utilizing R within the chapters are instructive and practical. Exercises that focus on real-world applications without sacrificing mathematical rigor are included, along with more than 200 figures that help clarify both concepts and applications. In addition, the book features two helpful appendices: annotated solutions to 700 exercises and a Review of Useful Math. Written for use in applied masters classes, Probability and Mathematical Statistics: Theory, Applications, and Practice in R is also suitable for advanced undergraduates and for self-study by applied mathematicians and statisticians and qualitatively inclined engineers and scientists.

Download or read book Python for Probability Statistics and Machine Learning written by José Unpingco and published by Springer. This book was released on 2019-06-29 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Download or read book Inside Mathematics Probability and Statistics written by Mike Goldsmith and published by . This book was released on 2021-09 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the hardest questions that mathematics teachers have to answer is "Why?" Schoolroom sums are crucial in learning the awesome power of mathematics, but they are often a world away from how the knowledge is applied and where it came from. Inside Mathematics: Probability & Statistics is there to fill that gap. What are the chances of that? Mathematics can solve that mystery for you using a set of ideas that grew out of an aristocratic gambler's bafflement at betting on complex dice games. In stepped the mathematical giants of Pierre de Fermat and Blaise Pascal, who worked together to create what is now called probability theory. Gamblers need not rejoice in this powerful theory; it shows that the casino always wins in the end. The ideas of probability have since found many better uses elsewhere. For example, they are at work in the mathematics that describes the quantum world and drives the push for artificial intelligence. The mathematics of chance is involved in understanding systems where a myriad data points combine. Statistics is the branch of mathematics that wrangles that data and tames it into meaningful knowledge. It then allows us to get ever better at modeling complex phenomena, from the formation of stars and the path of a hurricane to the rise and fall of the markets. Inside Mathematics: Probability & Statistics introduces the reader to these awesome mathematical powers by telling the stories of who figured them out. They include a cavalry officer hoping to reduce injuries from horse kicks, Charles Darwin's cousin who discovered that we make the best guesses when we work together, and computers that are built to program themselves. Written to engage and enthuse young people, Inside Mathematics shows readers how the ideas of long-dead geniuses have ended up in their homework assignments. Probability & Statistics: How Mathematics Can Predict the Future changes the question from "Why?" to "What's next?" Arranged chronologically to show how ideas in mathematics evolved.

Download or read book Algebra Through Applications with Probability and Statistics written by National Science Foundation Grant No. SED74-18948 and published by . This book was released on 1979-01-01 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Introduction to Probability written by Joseph K. Blitzstein and published by CRC Press. This book was released on 2014-07-24 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Download or read book Introduction to Probability written by John E. Freund and published by Courier Corporation. This book was released on 2012-05-11 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Featured topics include permutations and factorials, probabilities and odds, frequency interpretation, mathematical expectation, decision making, postulates of probability, rule of elimination, much more. Exercises with some solutions. Summary. 1973 edition.

Download or read book Statistics and Probability with Applications High School written by Daren S. Starnes and published by Macmillan Higher Education. This book was released on 2016-09-30 with total page 1532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics and Probability with Applications, Third Edition is the only introductory statistics text written by high school teachers for high school teachers and students. Daren Starnes, Josh Tabor, and the extended team of contributors bring their in-depth understanding of statistics and the challenges faced by high school students and teachers to development of the text and its accompanying suite of print and interactive resources for learning and instruction. A complete re-envisioning of the authors’ Statistics Through Applications, this new text covers the core content for the course in a series of brief, manageable lessons, making it easy for students and teachers to stay on pace. Throughout, new pedagogical tools and lively real-life examples help captivate students and prepare them to use statistics in college courses and in any career.