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» First-order probabilistic inference
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NIPS
2007
13 years 11 months ago
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smooth...
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy...
NIPS
2008
13 years 11 months ago
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
Indraneel Mukherjee, David M. Blei

Publication
252views
14 years 1 months ago
Context models on sequences of covers
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approac...
Christos Dimitrakakis
ECML
2007
Springer
14 years 1 months ago
Modeling Highway Traffic Volumes
Most traffic management and optimization tasks, such as accident detection or optimal vehicle routing, require an ability to adequately model, reason about and predict irregular an...
Tomás Singliar, Milos Hauskrecht
BMCBI
2005
169views more  BMCBI 2005»
13 years 9 months ago
Genetic interaction motif finding by expectation maximization - a novel statistical model for inferring gene modules from synthe
Background: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership...
Yan Qi 0003, Ping Ye, Joel S. Bader