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Publication details
Main information
Title:
Objective Bayesian calibration and the problem of non-convex evidence
Publication date:
2012
Citation:
OBE-coin
Abstract:
Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence. One role of the calibration norm is to ensure that evidence works to constrain a convex set of probability functions. This essay brings into focus a problem for Williamson's theory when qualitative evidence specifies non-convex constraints.
Journal
Authors:
Gregory Wheeler
Journal:
The British Journal for the Philosophy of Science
Publisher:
Oxford University Press
Address:
-
Volume:
63
Number:
4
Pages:
841-850
ISBN:
-
ISSN:
0007-0882
Note:
-
Url address:
-
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Plain text:
Gregory Wheeler, Objective Bayesian calibration and the problem of non-convex evidence, The British Journal for the Philosophy of Science, Vol. 63, No. 4, Pag. 841-850, Oxford University Press, ISSN 0007-0882, 2012.
HTML:
<b><a href="/people/members/view.php?code=768e30c2109e32d4ad41104cf0167777" class="author">Gregory Wheeler</a></b>, <u>Objective Bayesian calibration and the problem of non-convex evidence</u>, The British Journal for the Philosophy of Science, Vol. 63, No. 4, Pag. 841-850, Oxford University Press, ISSN 0007-0882, 2012.
BibTeX:
@article {OBE-coin, author = {Gregory Wheeler}, title = {Objective Bayesian calibration and the problem of non-convex evidence}, journal = {The British Journal for the Philosophy of Science}, publisher = {Oxford University Press}, volume = {63}, number = {4}, pages = {841-850}, issn = {0007-0882}, abstract = {Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence. One role of the calibration norm is to ensure that evidence works to constrain a convex set of probability functions. This essay brings into focus a problem for Williamson's theory when qualitative evidence specifies non-convex constraints.}, year = {2012}, }
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