Entropic criterion for model selection

✍ Scribed by Chih-Yuan Tseng

• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 159 KB
• Volume: 370
• Category: Article
• ISSN: 0378-4371
• DOI: 10.1016/j.physa.2006.03.024

✦ Synopsis

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.