General Hyperplane Prior Distributions Based on Geometric Invariances for Bayesian Multivariate Linear Regression

General Hyperplane Prior Distributions Based on Geometric Invariances for Bayesian Multivariate Linear Regression

Based on geometric invariance properties, we derive an explicit prior distribution for the parameters of multivariate linear regression problems in the absence of further prior information. The problem is formulated as a rotationally-invariant distribution of \(L\)-dimensional hyperplanes in \(N\) d...

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Bibliographic Details
Main Author: Udo von Toussaint
Format: Article
Language:English
Published: MDPI AG 2015-06-01
Series:Entropy
Subjects:
prior probabilities
hyperplanes
geometrical probability
neural networks
Online Access:http://www.mdpi.com/1099-4300/17/6/3898

Internet

http://www.mdpi.com/1099-4300/17/6/3898

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