Name
Abstract | ||
|---|---|---|
In this paper, we explore the use of exact inference in hybrid Bayesian networks to compute the exact marginal distribution of project completion time. Activities durations can have any distribution, and may not be all independent. We model dependence between activities using a Bayesian network, approximate non-Gaussian conditional distributions by mixtures of Gaussians, and reduce the resulting hybrid Bayesian network to a mixture of Gaussian Bayesian networks. Such hybrid Bayesian networks can be solved exactly using Hugin, a commercially-available software package. We illustrate our approach using a small PERT network with five activities. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.734 | ICNC |
Keywords | Field | DocType |
small pert network,hybrid bayesian network,exact inference,exact marginal distribution,bayesian network,hybrid bayesian networks,approximate non-gaussian conditional distribution,activities duration,model dependence,model dependent project scheduling,commercially-available software package,gaussian bayesian network,random variables,probability density function,scheduling,gaussian distribution,monte carlo methods,conditional distribution,pert,bayes net,bayesian methods,exponential distribution,mixture of gaussians,project scheduling | Mathematical optimization,Variable-order Bayesian network,Computer science,Bayesian linear regression,Bayesian network,Bayesian hierarchical modeling,Artificial intelligence,Bayesian statistics,Graphical model,Machine learning,Bayesian probability,Dynamic Bayesian network | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||