In this paper, we study the value distributions of perfect nonlinear functions, i.e., we investigate the sizes of image and preimage sets. Using purely combinatorial tools, we develop a framework that deals with perfect nonlinear functions in the mos...
We study a variant of the secretary problem where candidates come from independent, not necessarily identical distributions known to us, and show that we can do at least as well as in the IID setting. This resolves a conjecture of Esfandiari et al....
Fokker-Planck equations (forward Kolmogorov equations) evolve probability densities in time from an initial condition. For distributions over the real line, these evolution equations can sometimes be transformed into dynamics over the incomplete zero...
We develop a new methodology for the efficient computation of epidemic final size distributions for a broad class of Markovian models. We exploit a particular representation of the stochastic epidemic process to derive a method which is both computat...
The Generalized Parton Distributions (GPD) have drawn a lot of interest from the theoretical community since 1997, but also from the experimental community and especially at Jefferson Lab. First, the results for Deeply Virtual Compton Scattering (D...
Graph pebbling is the study of moving discrete pebbles from certain initial distributions on the vertices of a graph to various target distributions via pebbling moves. A pebbling move removes two pebbles from a vertex and places one pebble on one of...
Kullback Leibler (KL) Distance of Two Normal (Gaussian) Probability Distributions
We present results from simulations of regular pentagons arranged in a rectangular die. The particles are subjected to vertical tapping. We study the behavior of the packing fraction, number of contacts and arch distributions as a function of the t...
This paper investigates logical consequence defined in terms of probability distributions, for a classical propositional language using a standard notion of probability. We examine three distinct probabilistic consequence notions, which we call mater...
Preparation of Gibbs distributions is an important task for quantum computation. It is a necessary first step in some types of quantum simulations and further is essential for quantum algorithms such as quantum Boltzmann training. Despite this, most...
A statistical analysis of full text downloads of articles in Elseviers ScienceDirect covering all disciplines reveals large differences in download frequencies, their skewness, and their correlation with Scopus-based citation counts, between discipli...
Let B be a translation invariant Banach function space (BF-space). In this paper we prove that every temperate distribution f can be associated with a function F analytic in the convex tube Omega={z in C^d; |Im z|<1} such that the wave-front set...
Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unexpected side effects, as interpolation paths lie outside the area where...
Analysis of vote distributions using current tools from statistical physics is of increasing interest. While data considered for physics studies are subject to a careful understanding of error sources, such analysis are almost absent in studies of vo...
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …
Jul 10, 2017 · In "cross"-entropy, as the name suggests, we focus on the number of bits required to explain the difference in two different probability distributions. The best case scenario is that both …