Bayesian Analysis With Excel

Bayesian Methods can solve problems which can’t be reliably handled any other way. Building on existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps learners 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 how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, provides downloadable Excel workbooks that can easily adapt to learner’s needs, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan.

Features –

Explores key ideas and strategies that underlie Bayesian analysis

Distinguishes between prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs

Uses grid approximation to solve simple univariate problems, and understand its limits as parameters increase

Elaborates complex simulations and regressions with quadratic approximation and Richard McElreath’s quap function

Discusses today’s gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC)

Explains MCMC to optimize execution speed in high-complexity problems”