Dynamics & probability: Amitai Zernik (HUJI): A Diagrammatic Recipe for Computing Maxent Distributions

Let S be a finite set (the sample space), and 
f_i: S -> R functions, for 1 ≤ i ≤ k. Given a k-tuple (v_1,...,v_k) in R^k
it is natural to ask: 
What is the distribution P on S that maximizes the entropy
      -Σ P(x) log(P(x))
subject to the constraint that the expectation of f_i be v_i?
In this talk I'll discuss a closed formula for the solution P
in terms of a sum over cumulant trees. This is based on a general calculus
for solving perturbative optimization problems due to Feynman, which may be
of interest in its own right. 
The talk will be completely self-contained, requiring only rudimentary
knowledge of calculus and probability theory. This is joint work with Tomer
Schlank and Ran Tessler.


Tue, 14/06/2016 - 14:00 to 15:00


Manchester building, Hebrew University of Jerusalem, (Room 209)