This chapter has several rather practical purposes related to applied MCMC work: to

introduce formal convergence diagnostic techniques, to provide tools to improve mixing

and coverage, and to note a number of challenges that are routinely encountered. This

is a stark contrast to the last chapter, which was concerned with theoretical properties

of Markov chains and Markov chain Monte Carlo. Since applied work is generally done

computationally through the convenient programs BUGS (in any of the versions) and JAGS,

or by writing source code in R, C, or even Fortran, practical considerations are important

to getting reliable inferences from chain values. Most of the concern centers on assessing

convergence, but the speed of the sampler, and its ability to thoroughly explore the sample

space are also important issues to be concerned with. This chapter also describes the two

very similar R packages for analyzing MCMC output and evaluating convergence: BOA and

CODA. These are merely convenient functional routines, and users will often want to go

beyond their capabilities, particularly in graphics. However, the purpose here is mainly to

understand the key workings of these tools rather than to function as a detailed description

of the syntax of software. See Albert (2009) or Ntzoufras (2009) for recent book-length

works with very detailed R and BUGS code description.