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
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
