Normal Models

Abstract

This chapter uses the standard normal \(\mathcal{N}(\mu,{\sigma }^{2})\) distribution as an easy entry to generic Bayesian inferential methods. As in every subsequent chapter, we start with a description of the data used as a chapter benchmark for illustrating new methods and for testing assimilation of the techniques. We then propose a corresponding statistical model centered on the normal distribution and consider specific inferential questions to address at this level, namely parameter estimation, model choice, and outlier detection, once set the description of the Bayesian resolution of inferential problems. As befits a first chapter, we also introduce here general computational techniques known as Monte Carlo methods.