Maximum Entropy and Probability Kinematics Constrained by Conditionals
Stefan LukitsPhilosophy Department, University of British Columbia, 1866 Main Mall, Buchanan E370, Vancouver BC V6T 1Z1, Canada
Academic Editors: Juergen Landes and Jon Williamson
Entropy 2015, 17(4), 1690-1700; https://doi.org/10.3390/e17041690
Received: 15 November 2014 / Revised: 23 March 2015 / Accepted: 25 March 2015 / Published: 27 March 2015
(This article belongs to the Special Issue Maximum Entropy Applied to Inductive Logic and Reasoning)
Two open questions of inductive reasoning are solved: (1) does the principle of maximum entropy (PME) give a solution to the obverse Majerník problem; and (2) isWagner correct when he claims that Jeffrey’s updating principle (JUP) contradicts PME? Majerník shows that PME provides unique and plausible marginal probabilities, given conditional probabilities. The obverse problem posed here is whether PME also provides such conditional probabilities, given certain marginal probabilities. The theorem developed to solve the obverse Majerník problem demonstrates that in the special case introduced by Wagner PME does not contradict JUP, but elegantly generalizes it and offers a more integrated approach to probability updating.
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Keywords:
probability update; Jeffrey conditioning; principle of maximum entropy; formal epistemology; conditionals; probability kinematics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lukits, S. Maximum Entropy and Probability Kinematics Constrained by Conditionals. Entropy 2015, 17, 1690-1700.
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