For 35 years the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering applications. All aspects of probabilistic inference such as Techniques, Applications, and Foundations, are of interest. With the rapid growth of computing power, computational techniques such as Markov chain Monte Carlo sampling are of great interest, as are approximate inferential methods. Application areas include, but are not limited to: Astronomy and Astrophysics, Genetics, Geophysics, Medical Imaging, Material Science, Nanoscience, Source Separation, Particle Physics, Quantum Mechanics, Plasma Physics, Chemistry, Earth Science, Climate Studies, Engineering and Robotics. Foundational issues involving probability theory and information theory, and inference and inquiry are also of keen interest as there are yet many open questions.