Statistical Algorithms Book

The chapter on matrix algorithms summarises a large number of useful results. Algorithms for the most popular discrete and continuous statistical distributions appear in chapters 9 and 10. Estimation in a missing data setup is numerically exemplified in the chapter on Expectation Maximisation EM algorithm.

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning

Integrates up-to-date theoretical and algorithmic aspects of statistics under one roof. Starting with elementary algorithms on mean, median and mode, it thoroughly discusses variance, covariance, correlation, skewness and kurtosis measures, distance metrics, regression models, and variable selection methods.

A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation

quotThe book is suitable for readers who not only want to understand current statistical algorithms, but also gain a deeper understanding of how the algorithms are constructed and how they operate. It is addressed first and foremost to students and lecturers teaching the foundations of statistical algorithms.quot --Ivan Kiv, Zentralblatt MATH

Statistical mechanics algorithms and computations by Krauth, Werner Publication date 2006 Topics Statistical mechanics -- Data processing Publisher Oxford Oxford University Press Collection internetarchivebooks printdisabled Contributor Internet Archive Language English Item Size 685.3M

Probability and statistics Professional level algorithms Codes in MATLAB, Julia, and Python About the author Gilbert Strang is currently a Professor of Mathematics at MIT and has written six amazing books. 3. Naked Statistics Stripping the Dread from the Data This book is compiled in an extremely realistic tone that makes statistics come alive.

Book Features Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms. Matrix calculus methods for supporting machine learning analysis and design applications.

Key features Now in paperback and fortified with exercises, this book provides a course in modern statistical thinking written by two world-leading researchers 130 class-tested exercises covering theory, methods, and computation help students make the link to scientific knowledge and uncertainty Clarifies both traditional methods and current, popular algorithms e.g. neural nets, random

An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.