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, and offers sample R code to take advantage of the rethinking package…mehr
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, 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 organizationâ¿and yourself. Explore key ideas and strategies that underlie Bayesian analysisDistinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputsUse grid approximation to solve simple univariate problems, and understand its limits as parameters increasePerform complex simulations and regressions with quadratic approximation and Richard McElreath's quap functionManage text values as if they were numericLearn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC)Use MCMC to optimize execution speed in high-complexity problemsDiscover when frequentist methods fail and Bayesian methods are essentialâ¿and when to use both in tandem
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 analysisapproaches 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.
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Preface Chapter 1 Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors The Prior The Likelihood Contrasting a Frequentist Analysis with a Bayesian The Frequentist Approach The Bayesian Approach Summary Chapter 2 Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R's Binomial Functions Using R's dbinom Function Using R's pbinom Function Using R's qbinom Function Using R's rbinom Function Grappling with the Math Summary Chapter 3 Understanding the Beta Distribution Establishing the Beta Distribution in Excel Comparing the Beta Distribution with the Binomial Distribution Decoding Excel's Help Documentation for BETA.DIST Replicating the Analysis in R Understanding dbeta Understanding pbeta Understanding qbeta About Confidence Intervals Applying qbeta to Confidence Intervals Applying BETA.INV to Confidence Intervals Summary Chapter 4 Grid Approximation and the Beta Distribution More on Grid Approximation Setting the Prior Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Looking Behind the Curtains Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors Summary Chapter 5 Grid Approximation with Multiple Parameters Setting the Stage Global Options Local Variables Specifying the Order of Execution Normal Curves, Mu and Sigma Visualizing the Arrays Combining Mu and Sigma Putting the Data Together Calculating the Probabilities Folding in the Prior Inventorying the Results Viewing the Results from Different Perspectives Summary Chapter 6 Regression Using Bayesian Methods Regression a la Bayes Sample Regression Analysis Matrix Algebra Methods Understanding quap Continuing the Code A Full Example Designing the Multiple Regression Arranging a Bayesian Multiple Regression Summary Chapter 7 Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means Summary Chapter 8 MCMC Sampling Methods Quick Review of Bayesian Sampling Grid Approximation Quadratic Approximation MCMC Gets Up To Speed A Sample MCMC Analysis ulam's Output Validating the Results Getting Trace Plot Charts Summary and Concluding Thoughts Appendix Installation Instructions for RStan and the rethinking Package on the Windows Platform Glossary
Preface Chapter 1 Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors The Prior The Likelihood Contrasting a Frequentist Analysis with a Bayesian The Frequentist Approach The Bayesian Approach Summary Chapter 2 Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R's Binomial Functions Using R's dbinom Function Using R's pbinom Function Using R's qbinom Function Using R's rbinom Function Grappling with the Math Summary Chapter 3 Understanding the Beta Distribution Establishing the Beta Distribution in Excel Comparing the Beta Distribution with the Binomial Distribution Decoding Excel's Help Documentation for BETA.DIST Replicating the Analysis in R Understanding dbeta Understanding pbeta Understanding qbeta About Confidence Intervals Applying qbeta to Confidence Intervals Applying BETA.INV to Confidence Intervals Summary Chapter 4 Grid Approximation and the Beta Distribution More on Grid Approximation Setting the Prior Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Looking Behind the Curtains Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors Summary Chapter 5 Grid Approximation with Multiple Parameters Setting the Stage Global Options Local Variables Specifying the Order of Execution Normal Curves, Mu and Sigma Visualizing the Arrays Combining Mu and Sigma Putting the Data Together Calculating the Probabilities Folding in the Prior Inventorying the Results Viewing the Results from Different Perspectives Summary Chapter 6 Regression Using Bayesian Methods Regression a la Bayes Sample Regression Analysis Matrix Algebra Methods Understanding quap Continuing the Code A Full Example Designing the Multiple Regression Arranging a Bayesian Multiple Regression Summary Chapter 7 Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means Summary Chapter 8 MCMC Sampling Methods Quick Review of Bayesian Sampling Grid Approximation Quadratic Approximation MCMC Gets Up To Speed A Sample MCMC Analysis ulam's Output Validating the Results Getting Trace Plot Charts Summary and Concluding Thoughts Appendix Installation Instructions for RStan and the rethinking Package on the Windows Platform Glossary
