A limit theorem for one-dimensional Gibbs measures under conditions on the empirical field

Peter Menzel

Stochastic Processes and their Applications, 1993, vol. 44, issue 2, 347-359

Abstract: Starting from a principle of large deviations for the empirical field of a Gibbs measure, we prove a conditional limit theorem for one-dimensional Gibbs measures. Given that the empirical field lies in a subset of probability measures, the conditional Gibbs measures converge in some sense to the entropy minimizing measures of this subset.

Date: 1993
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(93)90031-X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:44:y:1993:i:2:p:347-359

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().