Publication
Performing Incremental Bayesian Inference by Dynamic Model Counting
AbstractThe ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 2005) demonstrates that the well-known Davis-Putnam procedure combined with a dynamic decomposition and caching technique is an effective method for exact inference in Bayesian networks with high density and width. In this paper, we define dynamic model counting and extend the dynamic decomposition and caching technique to multiple runs on a series of problems with similar structure. This allows us to perform Bayesian inference incrementally as the structure of the network changes. Experimental results show that our approach yields significant improvements over the previous model counting approaches on multiple challenging Bayesian network instances.
Download publicationRelated Resources
See what’s new.
2023
ANPL: Towards Natural Programming with Interactive DecompositionInteractive programming system ensures users can refine generated code…
2024
Autodesk Research’s George Fitzmaurice named ACM FellowResearch Fellow in Human Computer Interaction (HCI) and Visualization…
2014
Retrieving Causally Related Functions from Natural-language Text for Biomimetic DesignIdentifying biological analogies is a significant challenge in…
2015
Deploying CommunityCommands: A Software Command Recommender System Case StudyIn 2009 we presented the idea of using collaborative filtering within…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us