2011/01/18 by Facundo Bromberg, Bromberg, Facundo, Federico Schlüter +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1101.3381
openalex publication_date 2011/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work introduces the IB-score, a family of independence-based score functions for robust learning of Markov networks independence structures. Markov networks are a widely used graphical representation of probability distributions, with many applications in several fields of science. The main advantage of the IB-score is the possibility of computing it without the need of estimation of the numerical parameters, an NP-hard problem, usually solved through an approximate, data-intensive, iterative optimization. We derive a formal expression for the IB-score from first principles, mainly maximum a posteriori and conditional independence properties, and exemplify several instantiations of it, resulting in two novel algorithms for structure learning: IBMAP-HC and IBMAP-TS. Experimental results over both artificial and real world data show these algorithms achieve important error reductions in the learnt structures when compared with the state-of-the-art independence-based structure learning algorithm GSMN, achieving increments of more than 50% in the amount of independencies they encode correctly, and in some cases, learning correctly over 90% of the edges that GSMN learnt incorrectly. Theoretical analysis shows IBMAP-HC proceeds efficiently, achieving these improvements in a time polynomial to the number of random variables in the domain.