Bayesian Methods

TL;DR: This work points out problems with the assessment of country effects that appear not to be widely appreciated, and develops arguments using Monte Carlo simulation analysis of multilevel linear and logit models.

TL;DR: This chapter discusses Bayesian analysis for simple models, which focuses on the case for hierarchical modeling, and its applications in the Social Sciences and Simulation Based Bayesian Analysis.

TL;DR: A full structured review of applications of Bayesian methods to randomised controlled trials, observational studies, and the synthesis of evidence, in a form which should be reasonably straightforward to update is provided.

TL;DR: It is argued that a criterion for scientific acceptability of a prior distribution is that it be expressible as prior data, so that the strength of prior assumptions can be gauged by how much data they represent.