Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms

Autor:	Myers, James W., Laskey, Kathryn Blackmond, Levitt, Tod S.
Rok vydání:	2013
Předmět:	Computer Science - Artificial Intelligence Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results. Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1301.6726 Zobrazit plný text záznamu View this record from Arxiv