38 search results - page 4 / 8 » Mocapy - A toolkit for inference and learning in dynamic B... |
JMLR
2010
13 years 18 days ago
2010
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference pr...
JMLR
2010
13 years 18 days ago
2010
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
NIPS
1998
13 years 7 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 ...
NIPS
2004
13 years 7 months ago
2004
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
NIPS
2000
13 years 7 months ago
2000
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...