MaxEnt 2015 - 35th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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.
The workshop includes a one-day tutorial session, invited talks and 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. Abstracts (one page of about 400 words). Selected papers by the program committee will be edited and published in a book. All the papers will be in English.
Local Organizers
Adom Giffin Clarkson University
Carlo Cafaro Clarkson University
Kushani De silva Clarkson University
Kevin H. Knuth University at Albany (SUNY), USA
Advisory Committee
G. L. Bretthorst Washington Univ., USA
A. Caticha University at Albany (SUNY), USA
J. Center Autonomous Exploration, USA
G. Erickson Boise State Univ., USA
R. Fischer IPP, Germany
P. M.Goggans University of Mississippi, USA
K. H. Knuth University at Albany (SUNY), USA
T. Loredo Cornell Univ., USA
A. Mohammad-Djafari LSS-CNRS, France
C. Rodriguez University at Albany (SUNY), USA
J. Skilling Cambridge, UK
U. Toussaint IPP, Germany