most citedCritical Remarks on Single Link Search in Learning Belief Networks

39 citations · 61 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2013

Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?

Yang Xiang, Michael P. Beddoes, David L Poole

In this paper, the feasibility of using finite totally ordered probability models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is investigated. The general for…

cs.AI2013

Exploring Localization in Bayesian Networks for Large Expert Systems

Yang Xiang, David L. Poole, Michael P. Beddoes

Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At any time, a human reasoner in a large domain may…

cs.AI20138 cited

Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks

Yang Xiang

Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs) can be used for diagnosis of natural systems as well as for model-based diagnosis of artificial systems.…

cs.AI2013

A Method for Implementing a Probabilistic Model as a Relational Database

Michael S. K. M. Wong, C. J. Butz, Yang Xiang

This paper discusses a method for implementing a probabilistic inference system based on an extended relational data model. This model provides a unified approach for a variety of…

cs.AI201339 cited

Critical Remarks on Single Link Search in Learning Belief Networks

Yang Xiang, Michael S. K. M. Wong, N. Cercone

In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which d…

cs.AI201311 cited

Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels

Jun Hu, Yang Xiang

A pseudo independent (PI) model is a probabilistic domain model (PDM) where proper subsets of a set of collectively dependent variables display marginal independence. PI models can…