Bayesian Programming (Hardcover)

Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha

  • 出版商: CRC
  • 出版日期: 2013-12-20
  • 售價: $2,700
  • 貴賓價: 9.5$2,565
  • 語言: 英文
  • 頁數: 380
  • 裝訂: Hardcover
  • ISBN: 1439880328
  • ISBN-13: 9781439880326
  • 相關分類: 機率統計學 Probability-and-statistics
  • 立即出貨 (庫存=1)

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商品描述

Probability as an Alternative to Boolean Logic
While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data.

Decision-Making Tools and Methods for Incomplete and Uncertain Data
Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming.

Principles and Modeling
Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields.

Formalism and Algorithms
The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems.

FAQs
Along with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability.

The First Steps toward a Bayesian Computer
A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.

商品描述(中文翻譯)

機率作為布林邏輯的替代方案
儘管邏輯是理性推理的數學基礎和計算的基本原則,但它僅適用於信息完整且確定的問題。然而,許多現實世界的問題,從金融投資到郵件過濾,都是不完整或不確定的。機率理論和貝葉斯計算提供了一個替代框架來處理不完整和不確定的數據。

不完整和不確定數據的決策工具和方法
強調機率作為布林邏輯的替代方案,《貝葉斯編程》介紹了構建用於現實應用的概率程序的新方法。該書由設計和實施高效概率推理引擎以解釋貝葉斯程序的團隊撰寫,並提供了許多Python示例,這些示例也可在附加網站上找到,該網站還提供了一個解釋器,讓讀者可以嘗試這種新的編程方法。

原則和建模
只需要基礎的數學基礎,該書的前兩部分介紹了一種構建主觀概率模型的新方法。作者介紹了貝葉斯編程的原則,並討論了概率建模的良好實踐。許多簡單的示例突出了貝葉斯建模在不同領域中的應用。

形式和算法
第三部分綜合了貝葉斯推理算法的現有工作,因為需要一個高效的貝葉斯推理引擎來自動化貝葉斯程序中的概率計算。對於希望瞭解貝葉斯編程形式主義、主要概率模型、貝葉斯推理的通用算法以及學習問題的讀者,書中包含了許多參考文獻。

常見問題解答
第四部分除了詞彙表外,還包含了對常見問題的回答。作者比較了貝葉斯編程和可能性理論,討論了貝葉斯推理的計算複雜性,涵蓋了不完整性的不可簡化性,並討論了主觀主義與客觀主義的概率認識論。

邁向貝葉斯計算機的第一步
創建完整的貝葉斯計算框架需要新的建模方法、新的推理算法、新的編程語言和新的硬件。該書著重於方法論和算法,描述了邁向實現該目標的第一步。它鼓勵讀者探索新興領域,如生物啟發計算,並開發新的編程語言和硬件架構。