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{ "item_title" : "Operational Data Analytics", "item_author" : [" Qi Feng", "J. George Shanthikumar "], "item_description" : "This book develops Operational Data Analytics (ODA) as a rigorous theoretical foundation, a unified research methodology, and a guiding philosophy for data-integrated decision making. Moving beyond conventional approaches that emphasize general-purpose learning and optimization procedures, ODA places the structure of the decision problem at the heart of decision learning. Its central focus is the design and validation of data-to-decision mappings, termed operational statistics, that systematically exploit the knowledge and structural properties of the underlying system. Bringing together optimization, statistical inference, machine learning, and universal approximation, the authors develop a unified spectrum of decision-learning approaches distinguished by what is known, what remains to be learned, and how decision quality should be validated. The book equips researchers and doctoral students with a rigorous framework for integrating advanced analytical methods with mathematical structure and operational insight.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/246/878/3032468787_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Operational Data Analytics|Qi Feng

Operational Data Analytics : Foundations for Decision-Focused Regularized Learning

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Overview

This book develops Operational Data Analytics (ODA) as a rigorous theoretical foundation, a unified research methodology, and a guiding philosophy for data-integrated decision making. Moving beyond conventional approaches that emphasize general-purpose learning and optimization procedures, ODA places the structure of the decision problem at the heart of decision learning. Its central focus is the design and validation of data-to-decision mappings, termed operational statistics, that systematically exploit the knowledge and structural properties of the underlying system. Bringing together optimization, statistical inference, machine learning, and universal approximation, the authors develop a unified spectrum of decision-learning approaches distinguished by what is known, what remains to be learned, and how decision quality should be validated. The book equips researchers and doctoral students with a rigorous framework for integrating advanced analytical methods with mathematical structure and operational insight.

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Details

  • ISBN-13: 9783032468789
  • ISBN-10: 3032468787
  • Publisher: Springer
  • Publish Date: April 2027

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