Bayesian Analysis with Excel and R 1st Edition Conrad Carlberg

Bayesian Analysis with Excel and R 1st Edition Conrad Carlberg – Ebook Instant Download/Delivery ISBN(s): 9780137580989,0137580983, 9780137580897, 0137580894

Product details:

• ISBN 10: 0137580894
• ISBN 13: 9780137580897
• Author: Conrad Carlberg

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

Table contents:

1. Bayesian Analysis and R: An Overview
2. Generating Posterior Distributions with the Binomial Distribution
3. Understanding the Beta Distribution
4. Grid Approximation and the Beta Distribution
5. Grid Approximation with Multiple Parameters
6. Regression Using Bayesian Methods
7. Handling Nominal Variables
8. MCMC Sampling Methods

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