TY - GEN
T1 - Probabilistic constraint programming for parameters optimisation of generative models
AU - Zanin, Massimiliano
AU - Correia, Marco
AU - Cruz, Jorge
AU - Sousa, Pedro Alexandre da Costa
N1 - Sem pdf conforme despacho
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Brain dynamics
KW - Complex networks
KW - Generative models
KW - Probabilistic Constraint Programming
UR - http://www.scopus.com/inward/record.url?scp=84945922028&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23485-4_38
DO - 10.1007/978-3-319-23485-4_38
M3 - Conference contribution
AN - SCOPUS:84945922028
VL - 9273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 376
EP - 387
BT - Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PB - Springer Verlag
T2 - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015
Y2 - 8 September 2015 through 11 September 2015
ER -