Paper Info
Title
Unexpectedness and Bayes' Rule
Abstract
A great number of methods and of accounts of rationality consider at their foundations some form of Bayesian inference. Yet, Bayes' rule, because it relies upon probability theory, requires specific axioms to hold (e.g. a measurable space of events). This short document hypothesizes that Bayes' rule can be seen as a specific instance of a more general inferential template, that can be expressed also in terms of algorithmic complexities, namely through the measure of unexpectedness proposed by Simplicity Theory.
Year
DOI
Venue
2021
10.1007/978-3-031-12429-7-8
SOFTWARE ENGINEERING AND FORMAL METHODS: SEFM 2021 COLLOCATED WORKSHOPS
Keywords
DocType
Volume
Bayes' rule, Unexpectedness, Algorithmic complexity, Simplicity Theory, Computational cognitive model
Conference
13230
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Giovanni Sileno101.35
Jean-Louis Dessalles200.34