The annual workshop on Bayesian Inference and Maximum Entropy Methods in science and engineering (MaxEnt) promotes the development of theoretical and applied aspects of inductive logic.  Applied aspects deal with automated reasoning and decision making in the presence of uncertainty. Theoretical aspects of inductive logic provide a means of capturing fundamental aspects of theoretical physics. 

The four-day agenda includes a one-day tutorial session, invited lectures and papers, contributed papers and poster presentations. This workshop is being jointly sponsored by the University of Idaho, Division of Statistics and the Jaynes’ Foundation.

Call for papers for this years conference.

Tutorial Sessions

• Blind Signal Separation by Kevin Knuth

• Data and Image Fusion by Ali Mohammad-Djafari

• Maximum Entropy in the Mean: A Useful Tool for Constrained Inverse Problems by Henryk Gzyl