889 search results - page 48 / 178 » Observational Learning in Random Networks |
IPSN
2005
Springer
14 years 2 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...
AUSAI
2006
Springer
14 years 1 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...
CORR
2010
Springer
13 years 8 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...
CSB
2005
IEEE
14 years 2 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
13 years 9 months 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 ...