abstract bibtex

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.

@article{ title = {Bayesian network approach to making inferences in causal maps}, type = {article}, year = {2001}, pages = {479-498}, volume = {128}, publisher = {Elsevier Science B.V.}, id = {455231b6-35bc-392f-b111-bc54fa2c6cef}, created = {2015-04-11T19:07:34.000Z}, file_attached = {false}, profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0}, group_id = {35f66ed6-398e-3964-901f-42a3a90aaf10}, last_modified = {2017-03-14T14:28:30.967Z}, read = {false}, starred = {false}, authored = {false}, confirmed = {true}, hidden = {false}, private_publication = {false}, 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.}, bibtype = {article}, author = {Nadkarni, Sucheta and Shenoy, Prakash P.}, journal = {European Journal of Operational Research}, number = {3} }

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