Generalisations of Fisher Matrices
File(s)
Published version
Author(s)
Heavens, A
Type
Journal Article
Abstract
Fisher matrices play an important role in experimental design and in data analysis. Their primary role is to make predictions for the inference of model parameters—both their errors and covariances. In this short review, I outline a number of extensions to the simple Fisher matrix formalism, covering a number of recent developments in the field. These are: (a) situations where the data (in the form of ( x,y ) pairs) have errors in both x and y; (b) modifications to parameter inference in the presence of systematic errors, or through fixing the values of some model parameters; (c) Derivative Approximation for LIkelihoods (DALI) - higher-order expansions of the likelihood surface, going beyond the Gaussian shape approximation; (d) extensions of the Fisher-like formalism, to treat model selection problems with Bayesian evidence.
Date Issued
2016-06-22
Date Acceptance
2016-06-18
Citation
Entropy, 2016, 18 (6)
ISSN
1099-4300
Publisher
MDPI
Journal / Book Title
Entropy
Volume
18
Issue
6
Copyright Statement
© 2016 The Author. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Subjects
Science & Technology
Physical Sciences
Physics, Multidisciplinary
Physics
fisher matrices
statistics
experimental design
DARK ENERGY
PARAMETERS
RATIO
Fluids & Plasmas
01 Mathematical Sciences
02 Physical Sciences
Publication Status
Published
Article Number
236