Invariant Equivocation
(2017) In Erkenntnis 82(1). p.141-167- Abstract
Objective Bayesians hold that degrees of belief ought to be chosen in the set of probability functions calibrated with one’s evidence. The particular choice of degrees of belief is via some objective, i.e., not agent-dependent, inference process that, in general, selects the most equivocal probabilities from among those compatible with one’s evidence. Maximising entropy is what drives these inference processes in recent works by Williamson and Masterton though they disagree as to what should have its entropy maximised. With regard to the probability function one should adopt as one’s belief function, Williamson advocates selecting the probability function with greatest entropy compatible with one’s evidence while Masterton advocates... (More)
Objective Bayesians hold that degrees of belief ought to be chosen in the set of probability functions calibrated with one’s evidence. The particular choice of degrees of belief is via some objective, i.e., not agent-dependent, inference process that, in general, selects the most equivocal probabilities from among those compatible with one’s evidence. Maximising entropy is what drives these inference processes in recent works by Williamson and Masterton though they disagree as to what should have its entropy maximised. With regard to the probability function one should adopt as one’s belief function, Williamson advocates selecting the probability function with greatest entropy compatible with one’s evidence while Masterton advocates selecting the expected probability function relative to the density function with greatest entropy compatible with one’s evidence. In this paper we discuss the significant relative strengths of these two positions. In particular, Masterton’s original proposal is further developed and investigated to reveal its significant properties; including its equivalence to the centre of mass inference process and its ability to accommodate higher order evidence.
(Less)
- author
- Landes, Jürgen and Masterton, George LU
- organization
- publishing date
- 2017-02
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Erkenntnis
- volume
- 82
- issue
- 1
- pages
- 141 - 167
- publisher
- Springer
- external identifiers
-
- wos:000394209600008
- scopus:84964336877
- ISSN
- 0165-0106
- DOI
- 10.1007/s10670-016-9810-1
- language
- English
- LU publication?
- yes
- id
- 8f3f31f6-c182-4bee-a5ee-4c44cb5794ab
- date added to LUP
- 2016-09-30 14:40:32
- date last changed
- 2024-01-04 13:30:47
@article{8f3f31f6-c182-4bee-a5ee-4c44cb5794ab,
abstract = {{<p>Objective Bayesians hold that degrees of belief ought to be chosen in the set of probability functions calibrated with one’s evidence. The particular choice of degrees of belief is via some objective, i.e., not agent-dependent, inference process that, in general, selects the most equivocal probabilities from among those compatible with one’s evidence. Maximising entropy is what drives these inference processes in recent works by Williamson and Masterton though they disagree as to what should have its entropy maximised. With regard to the probability function one should adopt as one’s belief function, Williamson advocates selecting the probability function with greatest entropy compatible with one’s evidence while Masterton advocates selecting the expected probability function relative to the density function with greatest entropy compatible with one’s evidence. In this paper we discuss the significant relative strengths of these two positions. In particular, Masterton’s original proposal is further developed and investigated to reveal its significant properties; including its equivalence to the centre of mass inference process and its ability to accommodate higher order evidence.</p>}},
author = {{Landes, Jürgen and Masterton, George}},
issn = {{0165-0106}},
language = {{eng}},
number = {{1}},
pages = {{141--167}},
publisher = {{Springer}},
series = {{Erkenntnis}},
title = {{Invariant Equivocation}},
url = {{http://dx.doi.org/10.1007/s10670-016-9810-1}},
doi = {{10.1007/s10670-016-9810-1}},
volume = {{82}},
year = {{2017}},
}