evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. in this paper, bayesian belief networks (bbns) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. as a case study, we estimated the surface water evaporation of the persian gulf and worked with a dataset of observations (dai...

A Bayesain belief network is a graphical representation of the underlying probabilistic relationships of a complex system. These networks are used for reasoning with uncertainty, such as in decision support systems. This requires probabilistic inference with Bayesian belief networks. Simulation schemes for probabilistic inference with Bayesian belief networks offer many advantages over exact in...

Introduction In modeling real world tasks, one inevitably has to deal with uncertainty. This uncertainty is due to the fact that many facts are unknown and or simply ignored and summarized. Suppose that one morning you find out that your grass is wet. Is it due to rain, or is it due to the sprinkler? If there is no other information, you can only talk in terms of probabilities. In a probabilist...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...