2004/11/12 by Balaram Das, Das, Balaram · 2 citations
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.cs/0411034
openalex publication_date 2004/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The number of probability distributions required to populate a conditional probability table (CPT) in a Bayesian network, grows exponentially with the number of parent-nodes associated with that table. If the table is to be populated through knowledge elicited from a domain expert then the sheer magnitude of the task forms a considerable cognitive barrier. In this paper we devise an algorithm to populate the CPT while easing the extent of knowledge acquisition. The input to the algorithm consists of a set of weights that quantify the relative strengths of the influences of the parent-nodes on the child-node, and a set of probability distributions the number of which grows only linearly with the number of associated parent-nodes. These are elicited from the domain expert. The set of probabilities are obtained by taking into consideration the heuristics that experts use while arriving at probabilistic estimations. The algorithm is used to populate the CPT by computing appropriate weighted sums of the elicited distributions. We invoke the methods of information geometry to demonstrate how these weighted sums capture the expert's judgemental strategy.