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Bayesian learningIn 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.
Document ID
19900014766
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Denning, Peter J. (Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Date Acquired
September 6, 2013
Publication Date
March 6, 1989
Subject Category
Statistics And Probability
Report/Patent Number
NAS 1.26:181528
NASA-CR-181528
RIACS-TR-89.12
Accession Number
90N24082
Funding Number(s)
CONTRACT_GRANT: NCC2-387
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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