Bayesian Thinking
暫譯: 貝葉斯思維

Hoeflin, David A.

Bayesian Thinking
  • 出版商: Wiley
  • 出版日期: 2026-12-03
  • 售價: $5,720
  • 貴賓價: 9.5 折 $5,434
  • 語言: 英文
  • 頁數: 320
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1394450133
  • ISBN-13: 9781394450138
  • 相關分類: 機率統計學 Probability-and-statistics、R 語言

    • 海外代購書籍(需單獨結帳)

商品描述

Apply Bayesian analysis to real engineering, science, and business problems

Professionals facing uncertain data need a complete workflow from problem framing to defensible decisions. Bayesian Thinking: Case Studies and AI Tools to Support Data-Driven Decisions in Engineering, Science, and Business delivers that workflow through case studies in reliability engineering, medical diagnostics, economics, and product quality. Readers master every stage: framing real-world questions as probabilistic models, eliciting domain-informed priors, selecting models via Bayes factors and WAIC, running inference in R and Stan, and applying statistical decision theory to optimize actions.

The book shows how to partner with AI for prompt engineering, model exploration, and sensitivity analysis while preserving human judgment and rigor. Clear code snippets, visual diagnostics, and posterior predictive checks build intuition fast. You will learn to optimize maintenance schedules, evaluate diagnostic tests, model system performance, and extract actionable insights from noisy data under uncertainty.

  • Integrate artificial intelligence as a collaborative partner in Bayesian workflows for smarter prior elicitation, model exploration, and sensitivity analysis.
  • Elicit and calibrate informative priors from domain knowledge across engineering, medicine, and business with transparency and mathematical rigor.
  • Interpret posterior distributions confidently using visual diagnostics, trace plots, and posterior predictive checks during iterative model building.
  • Transform ambiguous real-world problems into formal Bayesian models ready for rigorous inference and statistical decision analysis.
  • Apply Bayesian methods to reliability challenges including failure analysis, maintenance optimization, and performance modeling of complex systems.

Reliability engineers, data analysts in manufacturing and aerospace, and analytics professionals will find immediate, practical value. Graduate students and researchers in statistics, industrial engineering, bioengineering, and economics gain a structured, code-driven approach to Bayesian modeling and decision-making under real-world uncertainty.

商品描述(中文翻譯)

運用 Bayesian 分析解決真實的工程、科學與商業問題

面對不確定資料的專業人士，需要一套從問題界定到提出具備充分論據之決策的完整工作流程。《Bayesian Thinking: Case Studies and AI Tools to Support Data-Driven Decisions in Engineering, Science, and Business》透過可靠度工程、醫療診斷、經濟學與產品品質等領域的案例研究，完整呈現這套工作流程。讀者將掌握每個階段：將真實世界的問題界定為機率模型、取得融入領域知識的先驗分布、透過 Bayes factor（貝氏因子）與 WAIC 選擇模型、使用 R 與 Stan 執行推論，以及應用統計決策理論來最佳化行動。

本書說明如何與 AI 協作，進行提示工程（prompt engineering）、模型探索與敏感度分析，同時保有人類的判斷力與分析嚴謹性。清楚的程式碼片段、視覺化診斷與後驗預測檢查（posterior predictive checks），能快速建立讀者的直覺理解。你將學會最佳化維護排程、評估診斷檢驗、建立系統效能模型，並從充滿雜訊且具有不確定性的資料中萃取可採取行動的洞見。

• 將人工智慧整合為 Bayesian 工作流程中的協作夥伴，以更有效地進行先驗分布 elicitation（先驗取得）、模型探索與敏感度分析。
• 在工程、醫學與商業領域中，根據領域知識取得並校準具資訊性的先驗分布，同時兼顧透明度與數學嚴謹性。
• 在反覆建構模型的過程中，運用視覺化診斷、trace plots（追蹤圖）與後驗預測檢查，自信地解讀後驗分布。
• 將模糊的真實世界問題轉換為正式的 Bayesian 模型，以進行嚴謹的推論與統計決策分析。
• 將 Bayesian 方法應用於可靠度挑戰，包括故障分析、維護最佳化，以及複雜系統的效能建模。

可靠度工程師、製造業與航太業的資料分析師，以及從事分析工作的專業人士，都能從本書中獲得立即且實用的價值。統計學、工業工程、生物工程與經濟學領域的研究生和研究人員，也能藉此建立一套結構化、以程式碼為核心的 Bayesian 建模方法，用於在真實世界的不確定性下進行決策。

作者簡介

David A. Hoeflin, PhD (Mathematics from Iowa State University), over his nearly thirty-year career, first at AT&T Bell Labs then AT&T Labs, rose to be a Director in the Optimization, Reliability and Customer Analytics department. He led the development of advanced statistical methods for network performance analysis, reliability engineering, and cybersecurity applications. He is a named inventor on multiple patents and has authored numerous peer-reviewed papers.

Michael Tortorella, PhD, retired as a Distinguished Member of Technical Staff at Bell Laboratories, where he worked for 26 years. He subsequently worked as a Research Professor in the Rutgers Center for Operations Research (RUTCOR) at Rutgers University and?as an Adjunct Professor at Stevens Institute of Technology. He holds the Ph.D. in mathematics from Purdue University and is a recognized expert in reliability engineering and probabilistic methods for complex systems.

He is the author of the Wiley textbook Reliability, Maintainability, and Supportability: Best Practices for Systems Engineers and has published extensively in reliability theory, engineering, and management, numerical analysis, and operations research.

作者簡介(中文翻譯)

David A. Hoeflin 博士（Iowa State University 數學博士）在近三十年的職業生涯中，先後任職於 AT&T Bell Labs 和 AT&T Labs，並晉升為 Optimization, Reliability and Customer Analytics 部門的主管。他帶領團隊開發用於網路效能分析、可靠度工程與資安應用的進階統計方法。他是多項專利的具名發明人，並發表過許多經同儕審查的論文。

Michael Tortorella 博士曾任 Bell Laboratories 傑出技術職員（Distinguished Member of Technical Staff），在該機構工作長達 26 年。之後，他曾擔任 Rutgers University Rutgers Center for Operations Research（RUTCOR）的研究教授，以及 Stevens Institute of Technology 的兼任教授。他持有 Purdue University 的數學博士學位，是複雜系統可靠度工程與機率方法領域公認的專家。

他是 Wiley 教科書《Reliability, Maintainability, and Supportability: Best Practices for Systems Engineers》的作者，並在可靠度理論、可靠度工程與管理、數值分析及作業研究等領域發表了大量研究成果。

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