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Bayesian Analysis with Excel and R (Final)Название: Bayesian Analysis with Excel and R (Final)
Автор: Conrad G. Carlberg
Издательство: Pearson Education, Inc.
Год: 2023
Страниц: 191
Язык: английский
Формат: pdf (true), epub
Размер: 16.3 MB

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.

This book has several aspects that I want to let you know about up front. If you’re already comfortable with terminology and concepts such as Hamiltonian Monte Carlo sampling, conjugate pairs, and posterior distributions, then this book is probably not for you. You already know a lot about those topics, and if you need more you know where to find it.

On the other hand, if you don’t feel quite at home with the purpose of random samples, R’s user interface, and why you might want to work with mean-corrected instead of with raw values, then it’s just possible that this book offers something that you might want to know about. Both this book and I assume that you have some background in statistical analysis—say, at the introductory college level, where you can expect to study some probability theory and how it applies to the assessment of sample means, variances, and correlations. Particularly if you have studied these problems in the past, you will be better placed to understand how Bayesian analysis differs from traditional approaches, and how it works out in the context of the functions and packages found in R. And if you feel as though you could use some refresher work in traditional statistical analysis, Pearson is making available to you for download an e-book titled Statistical Analysis: Microsoft Excel 2016. You’ll find details on obtaining that book at the end of this Preface.

You’re experienced. You probably have something close to the background in Bayesian analysis that I had in mind when I laid out the topics that I wanted this book to cover. It seemed to me that the world already has plenty of books about statistics and experimental methodology: one more isn’t going to help much. Something similar can be said about using syntax and diction that R recognizes: we already have as many elementary to intermediate texts on R as we need.

What we did need, I thought, was a source of information that connected the simplistic capabilities of VBA (the programming language historically offered by Microsoft Excel to give the user more control over the application) with the more sophisticated capabilities of programming languages such as R and C.

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

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