Probabilistic constraint programming for parameters optimisation of generative models

Massimiliano Zanin, Marco Correia, Jorge Cruz, Pedro Alexandre da Costa Sousa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Complex networks theory has commonly been used for modelling and understanding the interactions taking place between the elements composing complex systems. More recently, the use of generative models has gained momentum, as they allow identifying which forces and mechanisms are responsible for the appearance of given structural properties. In spite of this interest, several problems remain open, one of the most important being the design of robust mechanisms for finding the optimal parameters of a generative model, given a set of real networks. In this contribution, we address this problem by means of Probabilistic Constraint Programming. By using as an example the reconstruction of networks representing brain dynamics, we show how this approach is superior to other solutions, in that it allows a better characterisation of the parameters space, while requiring a significantly lower computational cost.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PublisherSpringer-Verlag
Pages376-387
Number of pages12
Volume9273
ISBN (Electronic)978-3-319-23484-7
DOIs
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
CountryPortugal
CityCoimbra
Period8/09/1511/09/15

Keywords

  • Brain dynamics
  • Complex networks
  • Generative models
  • Probabilistic Constraint Programming

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