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
Table of contents
Cover Page About This eBook Title Page Contents at a Glance Copyright Page Credits Pearson’s Commitment to Diversity, Equity, and Inclusion Contents About the Author Preface 1. Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors Contrasting a Frequentist Analysis with a Bayesian Summary 2. Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R’s Binomial Functions Grappling with the Math Summary 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 Summary 4. Grid Approximation and the Beta Distribution More on Grid Approximation Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors Summary 5. Grid Approximation with Multiple Parameters Setting the Stage Putting the Data Together Summary 6. Regression Using Bayesian Methods Regression à 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 7. Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means Summary 8. MCMC Sampling Methods Quick Review of Bayesian Sampling A Sample MCMC Analysis Summary and Concluding Thoughts A. Installation Instructions for RStan and the rethinking Package on the Windows Platform Glossary Index Code Snippets