454 search results - page 11 / 91 » Bayesian learning of measurement and structural models |
NN
1997
Springer
13 years 12 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
UAI
2003
13 years 9 months ago
2003
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
BMCBI
2010
13 years 7 months ago
2010
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
AUSAI
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
IDA
2003
Springer
14 years 28 days ago
2003
Springer
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...