Abstract
According to the principles of group theory, every group \(\mathcal G\) of transformations \(G_{\rho }\) can be represented by an isomorphic group \(\mathcal{G}_L\) of linear transformations \(\mathbf{G}_{\rho }\) of a vector space. The transformations of event and parameter, introduced in Chap. 6, are nonlinear. The probability distributions p and w are not elements of a vector space. Is it possible to define form invariance in terms of linear transformations of a vector space? This would give the possibility of distinguishing classes of transformations, such as orthogonal or unitary ones, that can occur in form invariance, from other ones that cannot occur. Section 6.4 has given a hint of the way of constructing linear representations: one must express probabilities by probability amplitudes. The present chapter pursues this route and defines linear representations of models with the symmetries of translation and dilation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See the introductory chapter of Hermann Hesse’s novel.
- 2.
Paul A. M. Dirac, 1902–1984, British physicist and Nobel laureate. He contributed to the foundation of quantum mechanics. He found a relativistically invariant form of it.
- 3.
The \(\delta \) distribution is often called the \(\delta \) function. However, under a reparameterisation, it behaves as a distribution, not as a function.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Harney, H.L. (2016). A Linear Representation of Form Invariance. In: Bayesian Inference. Springer, Cham. https://doi.org/10.1007/978-3-319-41644-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-41644-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41642-7
Online ISBN: 978-3-319-41644-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)