Seeking relationships in big data : A bayesian perspective

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Author(s)

  • Nozer D. Singpurwalla

Detail(s)

Original languageEnglish
Pages (from-to)116-121
Journal / PublicationInternational Journal of Management Science and Engineering Management
Volume11
Issue number2
Publication statusPublished - 2016

Abstract

The real purpose of collecting big data is to identify causality in the hope that this will facilitate credible predictivity. But the search for causality can trap one into infinite regress, and thus one takes refuge in seeking associations between variables in data sets. Regrettably, the mere knowledge of associations does not enable predictivity. Associations need to be embedded within the framework of the probability calculus to make coherent predictions. This is so because associations are a feature of probability models, and hence they do not exist outside the framework of a model. Measures of association, like correlation, regression, and mutual information merely refute a preconceived model. Estimated measures of associations do not lead to a probability model; a model is the product of pure thought. This paper discusses these and other fundamentals that are germane to seeking associations in particular, and machine learning in general.

Research Area(s)

  • Association, Bayesian, Big data, Correlation, Dependence