Bayesian Analysis with Excel and R

Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more. He explains how to use Bayesian analytics to solve a wide array of real problems, such as distinguishing prior, likelihood, and posterior distributions, using grid approximation to solve simple univariate problems, performing complex simulations and regressions with quadratic approximation and Richard McElreath's quap function, managing text values as if they were numeric, and using Markov Chain Monte Carlo (MCMC) to optimize execution speed in high-complexity problems.

Format: Paperback / softback
Length: 192 pages
Publication date: 30 January 2023
Publisher: Pearson Education (US)

Leverage the full power of Bayesian analysis for competitive advantage

Bayesian methods offer a unique approach to problem-solving that surpasses traditional methods in reliability and accuracy. By building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg empowers you to maximize the capabilities of Excel's Bayesian tools and transition towards R for even more advanced analysis.

Step-by-step, with real-world examples, Carlberg guides you through the application of Bayesian analytics to tackle a wide range of practical problems. He clarifies terminology that often confounds analysts, providing valuable insights into the world of Bayesian analysis. Additionally, he offers sample R code to leverage the rethinking package in R, offering a gateway to Stan, a powerful Bayesian software.

As you incorporate these Bayesian approaches into your analytical toolbox, you will cultivate a formidable competitive advantage for your organization, as well as for yourself.

Explore key ideas and strategies that underpin Bayesian analysis

Understand the distinctions between prior, likelihood, and posterior distributions, and explore different algorithms for driving sampling inputs.

Employ grid approximation to solve simple univariate problems, while appreciating its limitations as parameters increase.

Conduct complex simulations and regressions using quadratic approximation and Richard McElreath's quap function.

Manage text values as if they were numeric, unlocking valuable insights from qualitative data.

Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC).

Employ MCMC to optimize execution speed in high-complexity problems, ensuring efficient and accurate results.

Discover when frequentist methods fall short and Bayesian methods become essential, as well as when to use both approaches in tandem for optimal outcomes.

By leveraging the power of Bayesian analysis, you can unlock new levels of insight and decision-making, propelling your organization to success in an increasingly data-driven world.

Weight: 319g
Dimension: 230 x 176 x 11 (mm)
ISBN-13: 9780137580989