Decoupling, Sparsity, Randomization, and Objective Bayesian Inference

In this: publication
By this: publisher
In this Subject: Library Science
By this author: Stern, Julio

Author: Stern, Julio

Source: Cybernetics & Human Knowing, Volume 15, Number 2, 2008 , pp. 49-68(20)

Buy & download fulltext article:

Price: $28.46 plus tax (Refund Policy)

Abstract:

language="EN"><artinfo>Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST -Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in the epistemological framework of cognitive constructivism.

Keywords: Bayesian networks; Cognitive constructivism; Covariance structure; Decoupling; Randomization; Sparse factorization

Document Type: Research article

Publication date: 2008-01-01