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Invariant Equivocation

Landes, Jürgen and Masterton, George LU (2016) In Erkenntnis
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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Erkenntnis
pages
27 pages
publisher
Springer
external identifiers
  • 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
2016-09-30 14:40:32
@misc{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},
  month        = {04},
  pages        = {27},
  publisher    = {ARRAY(0xb1ba4c8)},
  series       = {Erkenntnis},
  title        = {Invariant Equivocation},
  url          = {http://dx.doi.org/10.1007/s10670-016-9810-1},
  year         = {2016},
}