Bayesian Network Representation of Joint Normal Distributions - Confounding Variables Model

4 Pages Posted: 21 Nov 2022

Date Written: November 5, 2022

Abstract

This article is the second of a series discussing the Bayesian Network representation of multivariate normal distributions. In the first article we introduced a cascading regressions model leading to a Bayesian network representation of any joint normal distribution [Pap22]. A joint normal distribution being fully specified by its mean vector and its covariance matrix is not simple to interact with as its Bayesian network equivalent. Representing a joint normal distribution as a Bayesian network enables visualizing and interact the distribution through the lens of probabilistic graphical models with TKRISKĀ®. We demonstrate in this article a simple yet powerful approach using a confounding variables model.

Keywords: Bayesian network, multivariate distribution, covariance

JEL Classification: C00, G13

Suggested Citation

Papaioannou, Denis, Bayesian Network Representation of Joint Normal Distributions - Confounding Variables Model (November 5, 2022). Available at SSRN: https://ssrn.com/abstract=4269267 or http://dx.doi.org/10.2139/ssrn.4269267

Denis Papaioannou (Contact Author)

Tenokonda ( email )

London
United Kingdom

HOME PAGE: http://www.tenokonda.com

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