menu
{ "item_title" : "Inverse Theory", "item_author" : [" Andreas Fichtner "], "item_description" : "How can we draw reliable conclusions from limited and imperfect data? This textbook offers a clear and accessible guide to the principles behind scientific inference, showing how a unifying framework connects fields as diverse as Earth science, medical imaging, non-destructive testing, meteorology, climate research, and machine learning. It presents both classical and modern methods for solving real-world inference problems, with practical guidance on evaluating the reliability of results and understanding their uncertainties. Designed as both a learning resource and a long-term reference, the book balances depth with clarity. Hands-on computational exercises throughout help readers translate ideas into practice, strengthen their intuition and build confidence in tackling their own data challenges. It is ideal for advanced undergraduate and postgraduate students, as well as researchers and professionals, across many disciplines, from environmental science and medical imaging to climate research, machine learning, and economics.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/00/955/238/1009552384_b.jpg", "price_data" : { "retail_price" : "75.00", "online_price" : "75.00", "our_price" : "75.00", "club_price" : "75.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Inverse Theory|Andreas Fichtner

Inverse Theory : The Art of Scientific Inference

PRE-ORDER NOW:
local_shippingShip to Me
Preorder. This item will be available on December 31, 2026 .
FREE Shipping for Club Members help

Overview

How can we draw reliable conclusions from limited and imperfect data? This textbook offers a clear and accessible guide to the principles behind scientific inference, showing how a unifying framework connects fields as diverse as Earth science, medical imaging, non-destructive testing, meteorology, climate research, and machine learning. It presents both classical and modern methods for solving real-world inference problems, with practical guidance on evaluating the reliability of results and understanding their uncertainties. Designed as both a learning resource and a long-term reference, the book balances depth with clarity. Hands-on computational exercises throughout help readers translate ideas into practice, strengthen their intuition and build confidence in tackling their own data challenges. It is ideal for advanced undergraduate and postgraduate students, as well as researchers and professionals, across many disciplines, from environmental science and medical imaging to climate research, machine learning, and economics.

This item is Non-Returnable

Details

  • ISBN-13: 9781009552387
  • ISBN-10: 1009552384
  • Publisher: Cambridge University Press
  • Publish Date: December 2026
  • Page Count: 488

Related Categories

You May Also Like...

    1

BAM Customer Reviews