CODA: convergence diagnosis and output analysis for MCMC

[1st paragraph] At first sight, Bayesian inference with Markov Chain Monte Carlo (MCMC) appears to be straightforward. The user defines a full probability model, perhaps using one of the programs discussed in this issue; an underlying sampling engine takes the model definition and returns a sequence of dependent samples from the posterior distribution of the model parameters, given the supplied data. The user can derive any summary of the posterior distribution from this sample. For example, to calculate a 95% credible interval for a parameter α, it suffices to take 1000 MCMC iterations of α and sort them so that α<sub>1</sub><α<sub>2</sub><...<α<sub>1000</sub>. The…

• NS
  National Science Foundation

  Award: 0350613
• HU
  Harvard University
• IF
  Institute for Quantitative Social Science, Harvard University
• DF
  Deutsche Forschungsgemeinschaft