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Boumuki, N. and Noda, T. (2016) On Gradient and Hamiltonian Flows on Even Dimensional Dually Flat Spaces. Fundamental Journal of Mathematics and Mathematical Sciences, 6, 51-66.

has been cited by the following article:

• 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.