Simplicity, Inference and Modelling

Keeping it Sophisticatedly Simple

Arnold Zellner editor Michael McAleer editor Hugo A Keuzenkamp editor

Format:Paperback

Publisher:Cambridge University Press

Published:15th Oct '09

Currently unavailable, and unfortunately no date known when it will be back

This paperback is available in another edition too:

Simplicity, Inference and Modelling cover

An inter-disciplinary perspective on the role of simplicity in modelling and inference, first published in 2002.

The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. Using a multidisciplinary perspective this 2002 monograph asks 'What is meant by simplicity?'The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics.

"This lively and informative exposition of several points of view...will make this book pleasurable reading for not only philosophers of science and epistemologists, but also for those data analysts interested in formalizing the foundations that guide and shape their modeling practices." Journal of American Statistical Association

ISBN: 9780521121354

Dimensions: 229mm x 152mm x 18mm

Weight: 470g

316 pages