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» Graphical Models: Statistical inference vs. determination
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PVLDB
2008
160views more  PVLDB 2008»
13 years 6 months ago
BayesStore: managing large, uncertain data repositories with probabilistic graphical models
Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications mus...
Daisy Zhe Wang, Eirinaios Michelakis, Minos N. Gar...
ISSTA
2010
ACM
13 years 9 months ago
Causal inference for statistical fault localization
This paper investigates the application of causal inference methodology for observational studies to software fault localization based on test outcomes and profiles. This methodo...
George K. Baah, Andy Podgurski, Mary Jean Harrold
NIPS
2000
13 years 8 months ago
The Manhattan World Assumption: Regularities in Scene Statistics which Enable Bayesian Inference
Preliminary work by the authors made use of the so-called "Manhattan world" assumption about the scene statistics of city and indoor scenes. This assumption stated that ...
James M. Coughlan, Alan L. Yuille
AI
2011
Springer
13 years 2 months ago
SampleSearch: Importance sampling in presence of determinism
The paper focuses on developing effective importance sampling algorithms for mixed probabilistic and deterministic graphical models. The use of importance sampling in such graphi...
Vibhav Gogate, Rina Dechter
ICML
2005
IEEE
14 years 8 months ago
A graphical model for chord progressions embedded in a psychoacoustic space
Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in...
David Barber, Douglas Eck, Jean-François Pa...