Bayesian analysis with Excel and R

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

This book explains the main differences between the basis for traditional, "frequentist" statistical methods and the basis for Bayesian approaches. The frequentist derives inferences from imagined populations and samples. In contrast, the Bayesian derives inferences from populations that are actually generated and then used as a source of samples. Three methods of generating Bayesian models are discussed: grid approximation, quadratic approximation and Markov Chain Monte Carlo (MCMC). The book walks the reader through R code that exemplifies each method, and shows how VBA and Excel can together perform grid approximation. The book recommends that the reader adopt Bayesian methods as an accompaniment to frequentist techniques.