Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Handbook of Computational Statistics: Concepts and Methods
For Weather, Flights, Runways of Airports all over the world, etc, click here.
  • Title: Handbook of Computational Statistics: Concepts and Methods
  • Author(s) James E. Gentle, Wolfgang HSrdle, et al
  • Publisher: Springer; 1 edition (August 26, 2004)
  • Hardcover: 900 pages
  • eBook: Google Books and PDF
  • Language: English
  • ISBN-10: 3540404643
  • ISBN-13: 978-3540404644
  • Share This:  

Book Description

This handbook cover the important subareas of computational statistics and give some flavor of the wide range of applications. It should be included in the library of any organization involved in any way with computational statistics. The editors and their authors deserve to be commended. Everyone concerned with computational statistics will want and need to consult this volume. It will be a considerable asset in the work of many a researcher and student of statistics. A definitive contribution that provokes applause stimulating further studies.

The Handbook of Computational Statistics - Concepts and Methods ist divided into 4 parts. It begins with an overview of the field of Computational Statistics, how it emerged as a seperate discipline, how it developed along the development of hard- and software, including a discussion of current active research.

The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment.

The third part focusses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.

Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.

About the Authors
  • N/A

Reviews, Ratings, and Recommendations: Related Books and Categories: Read and Download Links: Similar Books:
  • Think Stats, 2nd Edition: Exploratory Data Analysis in Python

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

  • Mostly Harmless Statistics (Rachel L. Webb)

    This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (GAISE).

  • Introduction to Modern Statistics (Mine Çetinkaya-Rundel, et al.)

    This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

  • Foundations in Statistical Reasoning (Pete Kaslik)

    This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

  • Theory of Statistics (James E. Gentle)

    This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics.

  • O'Reilly® Think Bayes: Bayesian Statistics in Python

    If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

  • Statistical Inference: Algorithms, Evidence, and Data Science

    A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.

  • Foundations of Descriptive and Inferential Statistics (H. van Elst)

    This book aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services.

  • Statistics Done Wrong: The Woefully Complete Guide (Reinhart)

    Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

  • Lies, Damned Lies: How to Tell the Truth with Statistics

    The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.

Book Categories
:
Other Categories
Resources and Links