26 search results - page 3 / 6 » Bayesian inference for a discretely observed stochastic kine... |
NIPS
1998
13 years 6 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 ...
PSIVT
2007
Springer
13 years 11 months ago
2007
Springer
This paper considers the problem of Bayesian inference in dynamical models with time-varying dimension. These models have been studied in the context of multiple target tracking pr...
IDA
2009
Springer
13 years 2 months ago
2009
Springer
We address the applicability of blind source separation (BSS) methods for the estimation of hidden influences in biological dynamic systems such as metabolic or gene regulatory net...
ALDT
2011
Springer
12 years 5 months ago
2011
Springer
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
JMLR
2010
13 years 2 hour ago
2010
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...