Field theories for learning probability distributions

Research output: Contribution to journalArticle

86 Scopus citations

Abstract

Imagine being shown N samples of random variables drawn independently from the same distribution. What can you say about the distribution? In general, of course, the answer is nothing, unless you have some prior notions about what to expect. From a Bayesian point of view one needs an a priori distribution on the space of possible probability distributions, which defines a scalar field theory. In one dimension, free field theory with a normalization constraint provides a tractable formulation of the problem, and we discuss generalizations to higher dimensions.

Original languageEnglish (US)
Pages (from-to)4693-4697
Number of pages5
JournalPhysical review letters
Volume77
Issue number23
DOIs
StatePublished - Jan 1 1996

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Field theories for learning probability distributions'. Together they form a unique fingerprint.

  • Cite this