John Skilling

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from VLDB 2026 / reviewers in the wild / expert

dblp:01/2355 · DBLP profile ↗

← Back

3ranked-venue papers

0as first author

0since 2021 · last 2005

0000-0001-8063-4473ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Probabilistic and Bayesian machine learning · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference bayesian computation	0.1	1	2005	Nested sampling for Potts models · NIPS 2005
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models markov random field	0.1	1	2005	Nested sampling for Potts models · NIPS 2005
Machine learning › Probabilistic and Bayesian machine learning › sampling nested sampling	0.1	1	2005	Nested sampling for Potts models · NIPS 2005
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › markov random field potts model	0.1	1	2005	Nested sampling for Potts models · NIPS 2005

Methods — techniques the papers use, named apart from their topics

nested sampling · 0.1monte carlo · 0.1

Year	Publication	Venue	Position
2005	Nested sampling for Potts models abstract Nested sampling is a new Monte Carlo method by Skilling [1] intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior sub ject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model. Iain Murray 0001, David J. C. MacKay, Zoubin Ghahramani, John Skilling	NIPS	4
1983	Image restoration by a powerful maximum entropy method S. F. Burch, Stephen F. Gull, John Skilling	Comput. Vis. Graph. Image Process.	3
1982	Image restoration by a powerful maximum entropy method S. F. Burch, Stephen F. Gull, John Skilling	Comput. Graph. Image Process.	3