scholarly journals Web image prediction using multivariate point processes

Author(s):  
Gunhee Kim ◽  
Li Fei-Fei ◽  
Eric P. Xing
1975 ◽  
Vol 12 (2) ◽  
pp. 383-389 ◽  
Author(s):  
D. J. Daley ◽  
R. K. Milne

Simple definitions and derivations of elementary properties are given for the various intensities and Palm-Khinchin functions associated with a multivariate point process.


1978 ◽  
Vol 10 (2) ◽  
pp. 411-430 ◽  
Author(s):  
Mark Berman

A class of stationary multivariate point processes is considered in which the events of one of the point processes act as regeneration points for the entire multivariate process. Some important properties of such processes are derived including the joint probability generating function for numbers of events in an interval of fixed length and the asymptotic behaviour of such processes. The general theory is then applied in three bivariate examples. Finally, some simple monotonicity results for stationary and renewal point processes (which are used in the second example) are proved in two appendices.


2018 ◽  
pp. 117-142 ◽  
Author(s):  
D.R. cox ◽  
Valerie Isham

1983 ◽  
Vol 20 (4) ◽  
pp. 884-890 ◽  
Author(s):  
Helmut Pruscha

The concept of a learning model (or random system with complete connections) with continuous time parameter is introduced on the basis of the notion of a multivariate point process possessing an intensity. The stepwise transition probabilities in terms of the intensity are derived and a Monte Carlo method for simulating a sample is presented. By modelling the intensity process various types of learning models can be built. We propose a linear learning model which comprises the continuous-time Markov process as well as Hawkes's mutually exciting point process. We study the asymptotic behaviour of this linear model in terms of explosion or extinction and of convergence of some estimates. We close with some numerical results from computer simulations.


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