Data Analytics, Computational Statistics, and Operations Research for Engineers : Methodologies and Applications

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Data Analytics, Computational Statistics, and Operations Research for Engineers : Methodologies and Applications

Samanta, Debabrata (EDT)/ Islam, SK Hafizul (EDT)/ Chilamkurti, Naveen (EDT)

ウェブストア価格 ¥13,268（本体¥12,062）
CRC Press（2024/10発売）
外貨定価 US$ 64.99
ポイント 120pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 246 p.
言語 ENG
商品コード 9780367715120
DDC分類 620.00151

Full Description

With the rapidly advancing fields of Data Analytics and Computational Statistics, it's important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.

Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information.

Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

Part 1: Statistical Computing. 1. Computational Arithmetic for Statistical Computation. 2. Numerical Algorithm and Software for Statistical Computation. 3. Impact of Modern Computer on Statistical Computing. 4. Numerical Methods as the Backbone of Simulation Techniques. 5. Linear Algebra and Optimization for Computation. 6. Role of Transformation Functions in Restructuring the Problem Statements. 7. Optimization of Computing Resources. 8. Role of Statistical Graphics in Data Analysis. Part 2: Statistical Methodology. 9. Computationally Intensive Statistical Methods. 10. Techniques in Computational Inferencing. 11. Computer Models for Design of Experiments. 12. Bayesian Analysis for Computational Inference. 13. Survival Analysis Models in Computational Methods. 14. Impact of Data Mining to the Computational Statistics for Machine Learning. Part 3: Computational Statistics Applications. 15. Computational Statistics in Finance and Economics. 16. Computationally Intensive Statistical Methods in Human Biology. 17. Computational Statistics within Clinical Research. 18. Computational Statistics for Network Security.