111 search results - page 19 / 23 » Causal Graphical Models with Latent Variables: Learning and ... |
ITCC
2005
IEEE
15 years 3 months ago
2005
IEEE
We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
105
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AAAI
2008
14 years 12 months ago
2008
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
102
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CORR
2012
Springer
13 years 5 months ago
2012
Springer
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are the most general conditions under...
IPPS
2006
IEEE
15 years 3 months ago
2006
IEEE
As an extension of Bayesian network, module network is an appropriate model for inferring causal network of a mass of variables from insufficient evidences. However learning such ...
NIPS
2008
14 years 11 months ago
2008
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...