An algorithm for determining the distribution of maximal entropy subject to constraints is presented. The method provides an alternative to the conventional procedure which requires the numerical solution of a set of implicit nonlinear equations for the Lagrange multipliers. Here they are determined by seeking a minimum of a concave function, a procedure which readily lends itself to computatio...

Maximum entropy inference can be used to find equations for the critical currents (Jc) in a type II superconductor as a function of temperature, applied magnetic field, and angle of the applied field, θ or . This approach provides an understanding of how the macroscopic critical currents arise from averaging over different sources of vortex pinning. The dependence of critical currents on tempe...

The Minimum Mutual Information (MinMI) Principle provides the least committed, maximum-joint-entropy (ME) inferential law that is compatible with prescribed marginal distributions and empirical cross constraints. Here, we estimate MI bounds (the MinMI values) generated by constraining sets Tcr comprehended by mcr linear and/or nonlinear joint expectations, computed from samples of N iid outcome...

Quantifying flow of information in a program involves calculating how much information (e.g. about secret inputs) can be leaked by observing the program’s public outputs. Recently this field has attracted a lot of research interest, most of which makes use of Shannon’s information theory, e.g. mutual information, conditional entropy, etc. Computability entails that any automated analysis of inf...

We propose a class of interior proximal-like algorithms for the second-order cone program which is to minimize a closed proper convex function subject to general second-order cone constraints. The class of methods uses a distance measure generated by a twice continuously differentiable strictly convex function on (0,+∞), and includes as a special case the entropy-like proximal algorithm [12] wh...

in this paper we introduce the concept of entropy operator for continuous systems of finite topological entropy. it is shown that it generates the kolmogorov entropy as a special case. if $phi$ is invertible then the entropy operator is bounded with the topological entropy of $phi$ as its norm.

Abstract. Shape constraints play an increasingly prominent role in nonparametric function estimation. While considerable recent attention has been focused on log concavity as a regularizing device in nonparametric density estimation, weaker forms of concavity constraints encompassing larger classes of densities have received less attention but offer some additional flexibility. Heavier tail beh...