Tractable models and efficient algorithms for Bayesian tomography

Bayesian methods have proven to be powerful tools for computed tomographic reconstruction in realistic physical problems. However, Bayesian methods require that a number of modeling and computational problems be addressed. The paper summarizes a coherent system of statistical modeling and optimization techniques designed to facilitate efficient, unsupervised Bayesian emission and transmission tomographic reconstruction. New results are included on improved convergence behavior of these methods