889 search results - page 48 / 178 » Observational Learning in Random Networks |
83
Voted
IPSN
2005
Springer
15 years 6 months ago
2005
Springer
We bound the number of sensors required to achieve a desired level of sensing accuracy in a discrete sensor network application (e.g. distributed detection). We model the state of...
113
Voted
AUSAI
2006
Springer
15 years 4 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...
89
Voted
CORR
2010
Springer
14 years 11 months ago
2010
Springer
We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the d...
104
Voted
CSB
2005
IEEE
15 years 6 months ago
2005
IEEE
The Ingenuity™ Pathways Knowledge Base (IPKB) contains over one million findings manually curated from the scientific literature. Highly-structured content from the IPKB forms...
TNN
2008
15 years 12 days ago
2008
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find ...