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En esta comunicación se establece una medida de la entropía contenida en un proceso puntual mediante el concepto de entropía de orden α y tipo β introducida por Sharma and Mittal (1975); quedando, de este modo, generalizada la entropía de McFadden. Una vez que se estudian las propiedades relativas a la tasa de cambio de la Entropía, se demuestra que el proceso de Poisson es el de Entropía máxima dentro de la clase de los procesos puntuales estacionarios.
Consider a stationary Boolean model with convex grains in and let any exposed lower tangent point of be shifted towards the hyperplane by the length of the part of the segment between the point and its projection onto the covered by . The resulting point process in the halfspace (the Laslett’s transform of ) is known to be stationary Poisson and of the same intensity as the original Boolean model. This result was first formulated for the planar Boolean model (see N. Cressie [Cressie])...
Observations are made on a point process in in a window of volume . The observation, or ‘score’ at a point , here denoted , is a function of the points within a random distance of . When the input is a Poisson or binomial point process, the large limit theory for the total score , when properly scaled and centered, is well understood. In this paper we establish general laws of large numbers, variance asymptotics, and central limit theorems for the total score for Gibbsian input ....
We study a Markov process on a system of interlacing particles. At large times the particles fill a domain that depends on a parameter ε > 0. The domain has two cusps, one pointing up and one pointing down. In the limit ε ↓ 0 the cusps touch, thus forming a tacnode. The main result of the paper is a derivation of the local correlation kernel around the tacnode in the transition regime ε ↓ 0. We also prove that the local process interpolates between the Pearcey process and the GUE minor process....
We show that any loop-free Markov chain on a discrete space can be viewed as a determinantal point process. As an application, we prove central limit theorems for the number of particles in a window for renewal processes and Markov renewal processes with Bernoulli noise.
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