An approach that is common in the machine learning literature, known as active sensing, is applied to provide a method for managing agile sensors in a dynamic environment. We adopt an active sensing approach to scheduling sensors for multiple target tracking applications that combines particle filtering, predictive density estimation, and relative entropy maximization. Specifically, the goal of...

We present a general holistic theory for the organization of complex networks, both human-engineered and naturally-evolved. Introducing concepts of ‘value’ of interactions and ‘satisfaction’ as generic network performance measures, we show that the underlying organizing principle is to meet an overall performance target for wide-ranging operating or environmental conditions. This design or surv...

This paper presents an agent-based complex system simulation of settlement structure change using methods derived from entropy maximization modeling. The approach is applied to model the movement of people and goods in urban settings to study how settlement size hierarchy develops. While entropy maximization is well known for assessing settlement structure change over different spatiotemporal s...

Traditionally, the Method of (Shannon-Kullback’s) Relative Entropy Maximization (REM) is considered with linear moment constraints. In this work, the method is studied under frequency moment constraints which are non-linear in probabilities. The constraints challenge some justifications of REM since a) axiomatic systems are developed for classical linear moment constraints, b) the feasible set ...

An approach to the Shannon and Rényi entropy maximization problems with constraints on the mean and law invariant deviation measure for a random variable has been developed. The approach is based on the representation of law invariant deviation measures through corresponding convex compact sets of nonnegative concave functions. A solution to the problem has been shown to have an alpha-concave d...

The variational principles called maximum entropy (MaxEnt) and maximum caliber (MaxCal) are reviewed. MaxEnt originated in the statistical physics of Boltzmann and Gibbs, as a theoretical tool for predicting the equilibrium states of thermal systems. Later, entropy maximization was also applied to matters of information, signal transmission, and image reconstruction. Recently, since the work of...