Sampling random graphs with given properties is a key step in the analysis of networks, as random ensembles represent basic null models required to identify patterns such as communities and motifs. A key requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that exactly match the enforced constraints. Unfortunately, w...

We provide an axiomatization of the simplest quantum system, namely the qubit, based on entropic principles. Specifically, we show: The qubit can be derived from the set of maximumentropy probabilities that satisfy an entropic version of the Heisenberg uncertainty principle. Our formulation is in phase space (following Wigner [41]) and makes use of Rényi [32] entropy (which includes Shannon [33...

Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...

We have investigated the nuclear density distributions in LaGaO3based perovskites, LaGaO3 (LG), (La0.9Sr0.1)GaO3(LSG), La(Ga0.9Mg0.1)O3(LGM), and (La0.8Sr0.2)(Ga0.8Mg0.15Co0.05)O3(LSGMC), to elucidate the ionic conduction path and disorder of oxide ions at elevated temperatures. Neutron diffraction data were collected in the temperature range from 26 to 1405oC in air, using a diffractometer HER...

We present a one-parameter family of approximation schemes, which we refer to as local maximumentropy approximation schemes, that bridges continuously two important limits: Delaunay triangulation and maximum-entropy (max-ent) statistical inference. Local max-ent approximation schemes represent a compromise—in the sense of Pareto optimality—between the competing objectives of unbiased statistica...