1. A method for predicting sand-grain composition and sand texture comprising: selecting a first set of system variables, said first set associated with sand-grain composition and sand texture; selecting a second set of system variables, said second set being directly or indirectly causally related to said first set of variables; obtaining or estimating data for each variable in the second set; forming a network with nodes comprising both sets of variables, having directional links connecting interdependent nodes, said directional links honoring known causality relationships; and using a Bayesian Network algorithm with said data to solve the network for said first set of variables and their associated uncertainties.
1. A method for predicting sand-grain composition and sand texture comprising: selecting a first set of system variables, said first set associated with sand-grain composition and sand texture; selecting a second set of system variables, said second set being directly or indirectly causally related to said first set of variables; obtaining or estimating data for each variable in the second set; forming a network with nodes comprising both sets of variables, having directional links connecting interdependent nodes, said directional links honoring known causality relationships; and using a Bayesian Network algorithm with said data to solve the network for said first set of variables and their associated uncertainties. 4. The method of claim 1 , where forming the network comprises: forming a third set of intermediate nodes interposed between at least some of the nodes representing the first set of system variables and at least some of the nodes representing the second set of system variables.