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, ...

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 ...

Abstract: This paper discusses the point process formalism of multiple target tracking problems. Finite point processes are defined as random elements in the spaces of finite sequences with their ...

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 ...

stppg.py includes basic generators for homogeneous and inhomogeneous univariate point process, as well as various types of intensity classes and kernel functions. utils.py includes plotting functions ...

Abstract: A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent ...

A determinantally-thinned (Poisson) point process is essentially a discrete determinantal point process whose underlying state space is a single realization of a (Poisson) point process defined on ...