most citedNetwork Fragments: Representing Knowledge for Constructing Probabilistic Models

164 citations

7 papers

cs.AI201371 cited

Network Engineering for Complex Belief Networks

Suzanne M. Mahoney, Kathryn Blackmond Laskey

Like any large system development effort, the construction of a complex belief network model requires systems engineering to manage the design and construction process. We propose…

cs.AI2013164 cited

Network Fragments: Representing Knowledge for Constructing Probabilistic Models

Kathryn Blackmond Laskey, Suzanne M. Mahoney

In most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex dom…

cs.AI201346 cited

Constructing Situation Specific Belief Networks

Suzanne M. Mahoney, Kathryn Blackmond Laskey

This paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments. A situation-specific network is a minimal query compl…

cs.AI201340 cited

Multiplicative Factorization of Noisy-Max

Masami Takikawa, Bruce D'Ambrosio

The noisy-or and its generalization noisy-max have been utilized to reduce the complexity of knowledge acquisition. In this paper, we present a new representation of noisy-max that…

cs.AI201311 cited

Representing and Combining Partially Specified CPTs

Suzanne M. Mahoney, Kathryn Blackmond Laskey

This paper extends previous work with network fragments and situation-specific network construction. We formally define the asymmetry network, an alternative representation for a c…

cs.AI201326 cited

Hypothesis Management in Situation-Specific Network Construction

Kathryn Blackmond Laskey, Suzanne M. Mahoney, Ed Wright

This paper considers the problem of knowledge-based model construction in the presence of uncertainty about the association of domain entities to random variables. Multi-entity Bay…