Bayesian Optimization with Application to Computer Experiments
暫譯: 貝葉斯優化在計算實驗中的應用

Pourmohamad, Tony, Lee, Herbert

Bayesian Optimization with Application to Computer Experiments
  • 出版商: Springer
  • 出版日期: 2021-10-05
  • 售價: $2,990
  • 貴賓價: 9.5 折 $2,841
  • 語言: 英文
  • 頁數: 90
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030824578
  • ISBN-13: 9783030824570
  • 相關分類: 機率統計學 Probability-and-statistics

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods.

Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field.

 

This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.

 

商品描述(中文翻譯)

這本書向讀者介紹貝葉斯優化，強調該領域的進展並展示其在計算實驗中的成功應用。大多數示例的 R 代碼作為線上補充材料提供，讓讀者能更好地理解和重現這些方法。

這本書簡潔易懂，共分為四個章節。第一章介紹計算實驗的主題，涵蓋了許多行業的各種示例。第二章專注於代理模型的建立，包含了計算建模和機器學習社群中使用的幾種不同的代理模型。第三章介紹貝葉斯優化的核心概念，並討論無約束優化。第四章則轉向有約束優化，展示了該領域中一些最具創新性的方法。

這本書將成為從事計算實驗和計算建模的研究人員和實務工作者的有用伴侶。此外，具有機器學習背景但對計算實驗了解不多的讀者，將會發現這本書是貝葉斯優化在機器學習領域之外應用的一個有趣案例研究。

作者簡介

Tony Pourmohamad is a principal statistical scientist in the Department of Biostatistics at Genentech. Prior to joining Genentech, he received his Ph.D. from the Department of Statistics and Applied Mathematics at the University of California, Santa Cruz, where his research focused on constrained optimization for computer experiments. Nowadays, he spends most of his time at the intersection of clinical and nonclinical statistics at Genentech.

Herbert Lee is Professor of Statistics in the Baskin School of Engineering at the University of California, Santa Cruz. He currently also serves as Vice Provost for Academic Affairs. He received his Ph.D. from the Department of Statistics at Carnegie Mellon University and completed a postdoc at Duke University. His research interests include Bayesian statistics, computer simulation experiments, inverse problems, and spatial statistics.

 

作者簡介(中文翻譯)

Tony Pourmohamad 是 Genentech 生物統計部的首席統計科學家。在加入 Genentech 之前，他在加州大學聖克魯斯分校的統計與應用數學系獲得博士學位，研究重點是針對計算機實驗的約束優化。如今，他大部分時間都在 Genentech 臨床與非臨床統計的交匯處工作。

Herbert Lee 是加州大學聖克魯斯分校巴斯金工程學院的統計學教授。他目前還擔任學術事務副教務長。他在卡內基梅隆大學的統計系獲得博士學位，並在杜克大學完成博士後研究。他的研究興趣包括貝葉斯統計、計算機模擬實驗、反問題和空間統計。

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