Bayesian Inference from Symplectic Geometric Viewpoint

Autor:	Hinako Matsuyama, Tomonori Noda
Rok vydání:	2019
Předmět:	Population mean Canonical transformation Multivariate normal distribution General Medicine Bayesian inference Algebra symbols.namesake Phase space symbols Affine transformation Hamiltonian (quantum mechanics) Mathematics::Symplectic Geometry Mathematics Symplectic geometry
Zdroj:	Advances in Pure Mathematics. :827-831
ISSN:	2160-0384 2160-0368
DOI:	10.4236/apm.2019.910039
Popis:	The purpose of this article is to formulate Bayesian updating from dynamical viewpoint. We prove that Bayesian updating for population mean vectors of multivariate normal distributions can be expressed as an affine symplectic transformation on a phase space with the canonical symplectic structure.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::09421ad4723e17c318596bb201058b31 https://doi.org/10.4236/apm.2019.910039 Zobrazit plný text záznamu