For over 37 years, the Max Ent 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.

The workshop will include a one-day tutorial session (Monday 2 July), invited papers, contributed papers and poster presentations. Contributed papers related to the above topics are being solicited. Especially encouraged are papers whose content is novel, either as to approach or area of application. Selected papers will be edited by the organizing committee and published in the proceedings. In addition, a special issue of the journal Entropy is planned that will be open to extended papers (invited and contributed) based on workshop contributions. These papers will be subjected to the journal's usual peer-review process.

Please visit the full conference website for more details on the agenda