Statistics With Rust Second Edition
Download Statistics With Rust Second Edition PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Statistics With Rust Second Edition book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Statistics with Rust, Second Edition
"Statistics with Rust, Second Edition" is designed to help you learn quickly, focusing on practical statistics using Rust scripts. The book is for readers who know the basics of statistics and machine learning. It gives quick explanations so you can try out concepts with hands-on coding. The book uses the newest version of Rust, 1.72.0, to help users build and secure statistical and machine learning algorithms. Each chapter is full of useful programs and code examples that will walk you through tasks like data manipulation, statistical tests, regression analysis, building machine learning models, and natural language processing. This second edition brings all chapters up to date with the latest in stats and Rust programming. It focuses on how you can put these things to practical use, with a detailed look at advanced algorithms like PCA, SVM, neural networks, and ensemble methods. We've also included some natural language processing topics, such as text preprocessing, tokenization, and word embeddings. The book also shows you how to combine Rust's performance and safety with statistical analysis, giving you the tools you need to do data analysis efficiently and reliably. The book's got lots of practical code and explanations that are easy to understand, which helps you learn the skills you need to get to grips with data using Rust. Table of Content Introduction to Rust for Statisticians Data Handling and Preprocessing Descriptive Statistics Probability Distributions and Random Variables Inferential Statistics Regression Analysis Bayesian Statistics Multivariate Statistical Methods Nonlinear Models and Machine Learning Model Evaluation and Validation Text and Natural Language Processing
Matrix Algebra
Author: James E. Gentle
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-07-27
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
Spatial Data Analysis in Ecology and Agriculture Using R
Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.