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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 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
Bayesian Analysis and R: An Overview
Generating Posterior Distributions with the Binomial Distribution
Understanding the Beta Distribution
Grid Approximation and the Beta Distribution
Grid Approximation with Multiple Parameters
Regression Using Bayesian Methods
Handling Nominal Variables
MCMC Sampling Methods
Appendix A