Format

Send to

Choose Destination
Proc Natl Acad Sci U S A. 2000 Oct 10;97(21):11170-5.

Counting probability distributions: differential geometry and model selection.

Author information

1
Department of Psychology, Ohio State University, 1885 Neil Avenue, Columbus, OH 43210-1222, USA. myung.1@osu.edu

Abstract

A central problem in science is deciding among competing explanations of data containing random errors. We argue that assessing the "complexity" of explanations is essential to a theoretically well-founded model selection procedure. We formulate model complexity in terms of the geometry of the space of probability distributions. Geometric complexity provides a clear intuitive understanding of several extant notions of model complexity. This approach allows us to reconceptualize the model selection problem as one of counting explanations that lie close to the "truth." We demonstrate the usefulness of the approach by applying it to the recovery of models in psychophysics.

PMID:
11005827
PMCID:
PMC17172
DOI:
10.1073/pnas.170283897
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center