Bayesian Inference from Symplectic Geometric Viewpoint

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DOI: 10.4236/apm.2019.910039    454 Downloads   1,049 Views  

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.

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Noda, T. and Matsuyama, H. (2019) Bayesian Inference from Symplectic Geometric Viewpoint. Advances in Pure Mathematics, 9, 827-831. doi: 10.4236/apm.2019.910039.

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