MAS1343: Introduction to statistical computingColin Gillespie
Its purpose was to offer an interactive approach to statistical computing, one that was user-friendly and made data analysis tasks easier and faster. In a 2013 interview, Chambers notes that the Bell Labs team wanted people to have “access to the best computational methods that existed, ...
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement ...
Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series) 作者: Keinosuke Fukunaga 出版社: Academic Press出版年: 1990-10页数: 592定价: USD 110.00装帧: HardcoverISBN: 9780122698514豆瓣评分 评价人数不足 ...
This book provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This...
Introduction to RLink R is a programming language and software environment for statistical computing and graphics. R is an open-source project, and a result of a large community effort. More information about R can be found at http://www.r-project.org. RLink is a Mathematica application tha...
Neural computing started in 1943 with the publication of a startling result by the American scientists Warren McCulloch and Walter Pitts. They considered the case of a network made up of binary decision units (BDNs). These are processes which emit a unit
statistical computing. In part because of the increasing amounts of data collected by software systems, and the need to analyze that data, R is one of the fastest-growing technologies among my colleagues who use C#. A familiarity with R can be a valuable addition to you...
(e.g., activate the solenoid valve if the humidity is below a threshold), might implement complex decision algorithms, might use real-time data and historical data, or might access to local data or data provided by web services (e.g., weather data, historical data, statistical data, etc....
We will also introduce some topics in statistical computing,such as EM,MCMC,varaitional inference,some optimization algorithm. Chapter 1 Introduction介绍 Machine Learning: supervised learning(监督学习) unsupervised learning(无监督学习) reinforcement learning(强化学习) Supervised Learning监督学习 The most...