Today, nearly every statistical analysis is performed on a computer. Some methods are particularly dependent on intensive computing or custom software. Biostatistics faculty are involved with this specialty, known as computational statistics. Some faculty analyze massive datasets. For example, in functional magnetic resonance imaging (fMRI) data, a single dataset consists of 100 million elements. Many faculty create software which is used throughout the world, including tools for the analysis of genetic data (e.g. for genotype error detection, and for linkage and association analysis in pedigrees) and brain imaging data (e.g. for nonparametric analysis of PET and fMRI data). Custom software is necessitated by complex data structures or for graphical methods for exploring data. Another area of interest to our faculty is permutation or resampling methods, which allow inferences under weak assumptions, but require analyzing variations on the data thousands of times over. An essential tool for Bayesian modeling is Markov Chain Monte Carlo (MCMC). This computationally intensive simulation procedure is used to characterize complex high-dimensional posterior distributions.