DTs: Dynamic Trees

Christopher KI Williams, Nicholas J Adams

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we introduce a new class of image models, which we call dynamic trees or DTs. A dynamic tree model specifies a prior over a large number of trees, each one of which is a tree-structured belief net (TSBN). Experiments show that DTs are capable of generating images that are less blocky, and the models have better translation invariance properties than a fixed, "balanced" TSBN. We also show that Simulated Annealing is effective at finding trees which have high posterior probability.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 11 (NIPS 1998)
PublisherMIT Press
Number of pages7
Publication statusPublished - 1999


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