Abstract: A method and apparatus for predicting sand-grain composition and sand texture are disclosed. A first set of system variables associated with sand-grain composition and sand texture is selected (605). A second set of system variables directly or indirectly causally related to the first set of variables is also selected (610). Data for each variable in the second set is estimated or obtained (615). A network with nodes including both sets of variables is formed (625). The network has a directional links connecting interdependent nodes. The directional links honor known causality relationships. A Bayesian network algorithm is used (630) with the data to solve the network for the first set of variables and their associated uncertainties.

Abstract: A method and apparatus are disclosed for modeling a system to estimate values and associated uncertainties for a first set of variables describing the system. A second set of system variables is selected, where the second set is directly or indirectly causally related to the first set of variables. Data is obtained or estimated for each variable in the second set and the quality of selected data is appraised. A network is formed with nodes including both sets of variables and the quality appraisals, having directional links connecting interdependent nodes, the directional links honoring known causality relationships. A Bayesian Network algorithm is used with the data and quality information to solve the network for the first set of variables and their associated uncertainties.

Abstract: A method and apparatus for predicting sand-grain composition and sand texture are disclosed. A first set of system variables associated with sand-grain composition and sand texture is selected (605). A second set of system variables directly or indirectly causally related to the first set of variables is also selected (610). Data for each variable in the second set is estimated or obtained (615). A network with nodes including both sets of variables is formed (625). The network has a directional links connecting interdependent nodes. The directional links honor known causality relationships. A Bayesian network algorithm is used (630) with the data to solve the network for the first set of variables and their associated uncertainties.