TY - GEN

T1 - On the number of samples needed to learn the correct structure of a Bayesian network

AU - Zuk, Or

AU - Margel, Shiri

AU - Domany, Eytan

PY - 2006

Y1 - 2006

N2 - Bayesian Networks (BNs) are useful tools giving a natural and compact representation of joint probability distributions. In many applications one needs to learn a Bayesian Network (BN) from data. In this context, it is important to understand the number of samples needed in order to guarantee a successful learning. Previous works have studied BNs sample complexity, yet they mainly focused on the requirement that the learned distribution will be close to the original distribution which generated the data. In this work, we study a different aspect of the learning task, namely the number of samples needed in order to learn the correct structure of the network. We give both asymptotic results (lower and upper-bounds) on the probability of learning a wrong structure, valid in the large sample limit, and experimental results, demonstrating the learning behavior for feasible sample sizes.

AB - Bayesian Networks (BNs) are useful tools giving a natural and compact representation of joint probability distributions. In many applications one needs to learn a Bayesian Network (BN) from data. In this context, it is important to understand the number of samples needed in order to guarantee a successful learning. Previous works have studied BNs sample complexity, yet they mainly focused on the requirement that the learned distribution will be close to the original distribution which generated the data. In this work, we study a different aspect of the learning task, namely the number of samples needed in order to learn the correct structure of the network. We give both asymptotic results (lower and upper-bounds) on the probability of learning a wrong structure, valid in the large sample limit, and experimental results, demonstrating the learning behavior for feasible sample sizes.

UR - http://www.scopus.com/inward/record.url?scp=79958826925&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:79958826925

SN - 0974903922

SN - 9780974903927

T3 - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

SP - 560

EP - 567

BT - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

T2 - 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Y2 - 13 July 2006 through 16 July 2006

ER -