"Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models

03/20/2013
by   John Mark Agosta, et al.
0

This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so generated has been termed "intercausal." It is shown by construction that inter-causal independence is possible for binary distributions at one state of evidence. For such "CICI" distributions, the two measures of inter-causal effect, "multiplicative synergy" and "additive synergy" are equal. The well known "noisy-or" model is an example of such a distribution. This introduces novel semantics for the noisy-or, as a model of the degree of conflict among competing hypotheses of a common observation.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

02/27/2013

Inter-causal Independence and Heterogeneous Factorization

It is well known that conditional independence can be used to factorize ...
02/06/2013

Structure and Parameter Learning for Causal Independence and Causal Interaction Models

This paper discusses causal independence models and a generalization of ...
03/03/2014

On the Intersection Property of Conditional Independence and its Application to Causal Discovery

This work investigates the intersection property of conditional independ...
03/15/2021

Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions

In the estimation of the causal effect under linear Structural Causal Mo...
04/22/2021

Minimizing the Sum of Age of Information and Transmission Cost under Stochastic Arrival Model

We consider a node-monitor pair, where updates are generated stochastica...
04/02/2020

Strong Converse for Testing Against Independence over a Noisy channel

A distributed binary hypothesis testing (HT) problem over a noisy channe...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.