Bayesian Inference and Maximum Entropy Methods in Science and Engineering

The papers for these proceedings were peer reviewed. Bayesian inference and maximum entropy methods provide a framework for analyzing very complicated data sets. The areas of application range from signal processing to thermodynamics. Maximum entropy is a theoretical method used to develop data when little information is available. Image enhancement of unclear astronomical data and medical images can be clarified using these methods. The papers in this volume provide applications of these methods to problems such as acoustics, fluids, thermodynamics, information theory, signal processing, astrophysics, medical imaging, proteins, pattern classification, and character recognition.