From certainty factors to belief networks

By David E. Heckerman, Edward H. Shortliffe. 1992

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “This problem can be prevented by adopting a rigorous probabilistic framework, such as the Bayesian estimation [28].”
State of the field: Measuring information and confirmation (2014, background): “…partD was also discussed in theoretical work on certainty factors, a central notion to represent uncertain reasoning in early expert systems (see Heckerman & Shortliffe, 1992; Shortliffe & Buchanan, 1975) and then more recently in data mining (see Glass, 2013; Greco, Slowinski, & Sczech, 2012)…”
Artificial intelligence in medicine (1989, unknown): “All authors should have made substantial contributions to all of the following: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the”
Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (2019, unknown): “All authors should have made substantial contributions to all of the following: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the”
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings (2017, unknown): “All authors should have made substantial contributions to all of the following: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the”
Emerging paradigms of cognition in medical decision-making (2002, background): “, stress, time pressure, and limited resources) found in many natural settings [57].”