TITLE:
Bayesian Inference from Symplectic Geometric Viewpoint
AUTHORS:
Tomonori Noda, Hinako Matsuyama
KEYWORDS:
Bayesian Inference, Hamiltonian, Symplectic, Canonical Transformation
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.9 No.10,
September
26,
2019
ABSTRACT: 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.