Discrete event simulation of point processes: A computational complexity analysis on sparse graphs

01/06/2020
by   Cyrille Mascart, et al.
0

We derive new discrete event simulation algorithms for marked time point processes. The main idea is to couple a special structure, namely the associated local independence graph, as defined by Didelez arXiv:0710.5874, with the activity tracking algorithm arXiv:arch-ive/190102412629 for achieving high performance asynchronous simulations. With respect to classical algorithm, this allows reducing drastically the computational complexity, especially when the graph is sparse.

READ FULL TEXT

page 23

page 24

research
03/20/2020

Event-based Asynchronous Sparse Convolutional Networks

Event cameras are bio-inspired sensors that respond to per-pixel brightn...
research
08/18/2022

Simulation methods and error analysis for trawl processes and ambit fields

Trawl processes are continuous-time, stationary and infinitely divisible...
research
03/31/2022

AEGNN: Asynchronous Event-based Graph Neural Networks

The best performing learning algorithms devised for event cameras work b...
research
05/31/2023

Estimation of Multivariate Discrete Hawkes Processes: An Application to Incident Monitoring

Hawkes processes are a class of self-exciting point processes that are u...
research
07/21/2020

Fast Graphlet Transform of Sparse Graphs

We introduce the computational problem of graphlet transform of a sparse...
research
02/27/2013

Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependencies

The paper presents a method for reducing the computational complexity of...
research
01/21/2018

Decoupled Learning for Factorial Marked Temporal Point Processes

This paper introduces the factorial marked temporal point process model ...

Please sign up or login with your details

Forgot password? Click here to reset