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Use of Linear Weights in BBN Modelling

IP.com Disclosure Number: IPCOM000012495D
Original Publication Date: 2003-May-12
Included in the Prior Art Database: 2003-May-12

Publishing Venue

Motorola

Related People

Authors:
Jean-Jacques Gras David McGaw Qiang Fu

Abstract

Bayesian Belief Network models (BBNs or BNs) can be constructed directly from data, but also through elicitation sessions with domain experts. The process involves painstaking discussions with the experts to quantify the probabilistic relationships (conditional probability) for each Node Probability Table (NPT). This can be intractable for nodes with a high number of states, especially continuous nodes. We propose the use of linear weights in BBN modelling as, often, the relation between continuous factors can be approximated by a linear function. The elicitation and construction of the node probability table is then fast and simple.