The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators ...

[Ch21] A. V. S. Chauhan, S. Reddy, M. Singh, K. Singh, T. Bhowmik. Deviation-based Marked Temporal Point process for Marker Prediction. (2021). [Du16] N. Du, H. Dai ...

Spatial point processes constitute a fundamental statistical framework for modelling the spatial distribution of events across a continuum. This field bridges theoretical developments in probability ...

Temporal point process, an important area in stochastic process, has been extensively studied in both theory and applications. The classical theory on point process focuses on time-based framework, ...

Point processes are stochastic processes with a wide range of applications in seismology, epidemiology, or financial mathematics. They are utilized to model the arrival of random events as a function ...

Hawkes processes represent a class of self-exciting point processes wherein each event increases the likelihood of subsequent events occurring over a short period. Initially developed by Alan Hawkes ...

This is a preview. Log in through your library . Abstract Consider a homogeneous Poisson point process of the Euclidean plane and its Voronoi tessellation. The present note discusses the properties of ...

Abstract: Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural ...

Operations Research, Vol. 43, No. 1, Special Issue on Telecommunications Systems: Modeling, Analysis and Design (Jan. - Feb., 1995), pp. 117-129 (13 pages) Unlike the leaky-bucket scheme which ...