Bayesian Analysis with Excel and R

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Leverage the Full Power of Bayesian Analysis for Competitive AdvantageBayesian methods can solve problems you cant 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 Excels 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, youll build a powerful competitive advantage for your organizationand 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 McElreaths quap functionManage text values as if they were numericLearn todays 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 essentialand when to use both in tandem