For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering applications. The workshop invites contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. In previous workshops, areas of application have included astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, material science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics and social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling have been regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, and the novel application of inference to illuminate the foundations of physical theories, have also been of keen interest.