Running summarizing tools on a new article

Abstract: This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from intractable full-conditional distributions of the person and item parameters in an application of the Gibbs sampler.