A Bayesian network approach to making inferences in causal maps

A Bayesian network approach to making inferences in causal maps

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Article ID: iaor20014239
Country: Netherlands
Volume: 128
Issue: 3
Start Page Number: 479
End Page Number: 498
Publication Date: Feb 2001
Journal: European Journal of Operational Research
Authors: ,
Keywords: Bayesian modelling, cognitive mapping
Abstract:

The main goal of this paper is to describe a new graphical structure called ‘Bayesian causal maps’ to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert's cognition. It is also a Bayesian network, i.e., a graphical representation of an expert's knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modified to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.

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