215 search results - page 14 / 43 » Coarse-to-Fine Inference and Learning for First-Order Probab... |
ICASSP
2010
IEEE
13 years 8 months ago
2010
IEEE
Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic const...
BMCBI
2010
13 years 7 months ago
2010
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
UM
2005
Springer
14 years 1 months ago
2005
Springer
Inference and decision making with probabilistic user models may be infeasible on portable devices such as cell phones. We highlight the opportunity for storing and using precomput...
JMLR
2010
13 years 2 months ago
2010
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
NIPS
1998
13 years 9 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...