Bayesian Analysis with Excel and R (使用 Excel 和 R 的貝葉斯分析)

Carlberg, Conrad

Bayesian Analysis with Excel and R

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商品描述

Leverage the full power of Bayesian analysis for competitive advantage

Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more.

Step by step, with real-world examples, Carlberg shows you how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, provides downloadable Excel workbooks you can easily adapt to your own needs, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan.

As you incorporate these Bayesian approaches into your analytical toolbox, you'll build a powerful competitive advantage for your organization---and yourself.

  • Explore key ideas and strategies that underlie Bayesian analysis
  • Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs
  • Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase
  • Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function
  • Manage text values as if they were numeric
  • Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC)
  • Use MCMC to optimize execution speed in high-complexity problems
  • Discover when frequentist methods fail and Bayesian methods are essential---and when to use both in tandem

商品描述(中文翻譯)

充分利用貝葉斯分析的強大優勢

貝葉斯方法可以解決其他方法無法可靠處理的問題。在您現有的Excel分析技能和經驗的基礎上，微軟Excel MVP Conrad Carlberg幫助您充分利用Excel的貝葉斯能力，並進一步轉向R以實現更多功能。

Carlberg通過實際案例逐步向您展示如何使用貝葉斯分析來解決各種實際問題。Carlberg釐清了常常讓分析師困惑的術語，提供可下載的Excel工作簿，您可以輕鬆適應自己的需求，並提供了使用R中的rethinking套件及其與Stan的連接的示例代碼。

當您將這些貝葉斯方法納入您的分析工具箱時，您將為您的組織和自己建立強大的競爭優勢。

- 探索貝葉斯分析的關鍵思想和策略
- 區分先驗、似然和後驗分佈，並比較驅動抽樣輸入的算法
- 使用網格近似法解決簡單的單變量問題，並了解隨著參數增加其限制
- 使用二次近似法和Richard McElreath的quap函數進行複雜的模擬和回歸
- 將文本值管理為數值
- 學習當今黃金標準的貝葉斯抽樣技術：馬爾可夫鏈蒙特卡羅（MCMC）
- 在高複雜性問題中使用MCMC來優化執行速度
- 發現頻率主義方法失敗並且貝葉斯方法是必不可少的時機，以及何時同時使用兩者。

作者簡介

Conrad Carlberg is a nationally recognized expert on quantitative analysis, data analysis, and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsoft's Excel MVP designation. He is the author of many books, including Business Analysis with Microsoft Excel, Fifth Edition, Statistical Analysis: Microsoft Excel 2016, Regression Analysis Microsoft Excel, and R for Microsoft Excel Users.

Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola.

In 1995 he started a small consulting business (www.conradcarlberg.com), which provides design and analysis services to companies that want to guide their business decisions by means of quantitative analysis--approaches that today we group under the term "analytics." He enjoys writing about those techniques and, in particular, how to carry them out using the world's most popular numeric analysis application, Microsoft Excel.

作者簡介(中文翻譯)

Conrad Carlberg是一位在量化分析、數據分析和管理應用領域（如Microsoft Excel、SAS和Oracle）上享有國家級聲譽的專家。他擁有科羅拉多大學的統計學博士學位，並多次獲得Microsoft Excel MVP的稱號。他是許多書籍的作者，包括《Business Analysis with Microsoft Excel》第五版、《Statistical Analysis: Microsoft Excel 2016》、《Regression Analysis Microsoft Excel》和《R for Microsoft Excel Users》。

Carlberg是加州南部的本地人。大學畢業後，他搬到科羅拉多，在一系列初創企業工作並攻讀研究生課程。他在中東待了兩年，教授計算機科學並躲避著脾氣暴躁的駱駝。在完成研究生課程後，Carlberg在美國西部（一家Baby Bell公司）擔任產品管理職位，並在摩托羅拉工作。

1995年，他開始了一家小型咨詢業務（www.conradcarlberg.com），為希望通過量化分析來指導業務決策的公司提供設計和分析服務，這些方法如今被歸為“分析”。他喜歡寫作這些技術，尤其是如何使用全球最受歡迎的數值分析應用程序Microsoft Excel進行分析。

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