Bayesian models for DNA sequencing

It is becoming increasingly important to develop novel signal processing and statistical analysis techniques to extract information from biotechnology. This task is complicated by large datasets, intricate physical systems, and the sheer diversity of information that is available. In many systems, classical non-parametric signal processing techniques have been applied with some success. However, where sufficient information is available to construct accurate models, substantial gains can sometimes be derived from a model-based approach. The Bayesian paradigm provides an elegant and mathematically rigorous framework for the objective incorporation of information. In this paper, we develop a Bayesian model for DNA sequencing, with an emphasis on generally relevant Bayesian model selection issues.