These proceedings from the 37th International Workshop on Bayesian Inference and Maximum
Entropy Methods in Science and Engineering (MaxEnt 2017) held in São Carlos Brazil aim to
expand the available research on Bayesian methods and promote their application in the
scientific community. They gather research from scholars in many different fields who use
inductive statistics methods and focus on the foundations of the Bayesian paradigm their
comparison to objectivistic or frequentist statistics counterparts and their appropriate
applications. Interest in the foundations of inductive statistics has been growing with the
increasing availability of Bayesian methodological alternatives and scientists now face much
more difficult choices in finding the optimal methods to apply to their problems. By carefully
examining and discussing the relevant foundations the scientific community can avoid applying
Bayesian methods on a merely ad hoc basis. For over 35 years the MaxEnt workshops have
explored the use of Bayesian and Maximum Entropy methods in scientific and engineering
application contexts. The workshops welcome contributions on all aspects of probabilistic
inference including novel techniques and applications and work that sheds new light on the
foundations of inference. Areas of application in these workshops include astronomy and
astrophysics chemistry communications theory cosmology climate studies earth science
fluid mechanics genetics geophysics machine learning materials science medical imaging
nanoscience source separation thermodynamics (equilibrium and non-equilibrium) particle
physics plasma physics quantum mechanics robotics and the social sciences. Bayesian
computational techniques such as Markov chain Monte Carlo sampling are also regular topics as
are approximate inferential methods. Foundational issues involving probability theory and
information theory as well as novel applications of inference to illuminate the foundations of
physical theories are also of keen interest.
