Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

Files in This Item:
File Description SizeFormat 
insight_publication.pdf436.76 kBAdobe PDFDownload
Title: Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Authors: Friel, Nial
Stoer, Julien
Permanent link:
Date: 12-May-2015
Abstract: Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods offer a principled means to construct useful approximations. This paper provides a mean to calibrate the posterior distribution resulting from using a composite likelihood and illustrate its performance in several examples.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Microtome Publishing
Copyright (published version): 2015 the authors
Keywords: Machine learningStatistics
Language: en
Status of Item: Peer reviewed
Is part of: 18th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA, 9-12 May 2015
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

Show full item record

Page view(s) 50

checked on May 25, 2018

Download(s) 50

checked on May 25, 2018

Google ScholarTM


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.