14. A method for generating probabilistic models, comprising: receiving by a processing device expert knowledge and data entered via an editing device; storing the received expert knowledge and data into one or more tables of a database system, a first subset of the data being represented in the one or more tables as conclusion data, a second subset of the data being represented as observation data, and a third subset of the data being represented as relationship data; generating by the processing device a probabilistic model from the one or more tables comprising: generating a structure of layers of nodes with at least one grouping layer disposed in-between a conclusion layer having a plurality of conclusion nodes designated as parent nodes and formed based on the stored conclusion data, and an evidence layer having a plurality of observation nodes designated as children nodes and formed based on the stored observation data, wherein a group node in the at least one grouping layer represents an aggregation of conclusions in two or more of the conclusion nodes in the conclusion layer, and generating, based on the stored relationship data, directed arcs only between nodes of non-identical layers, wherein at least one of the arcs is between the group node and at least one of the plurality of observation nodes, wherein, the generated probabilistic model is configured to be invoked by reasoning engine for outputting probabilities of one or more conclusions or aggregation of the conclusions, in response to input observations, during a decision making process.
14. A method for generating probabilistic models, comprising: receiving by a processing device expert knowledge and data entered via an editing device; storing the received expert knowledge and data into one or more tables of a database system, a first subset of the data being represented in the one or more tables as conclusion data, a second subset of the data being represented as observation data, and a third subset of the data being represented as relationship data; generating by the processing device a probabilistic model from the one or more tables comprising: generating a structure of layers of nodes with at least one grouping layer disposed in-between a conclusion layer having a plurality of conclusion nodes designated as parent nodes and formed based on the stored conclusion data, and an evidence layer having a plurality of observation nodes designated as children nodes and formed based on the stored observation data, wherein a group node in the at least one grouping layer represents an aggregation of conclusions in two or more of the conclusion nodes in the conclusion layer, and generating, based on the stored relationship data, directed arcs only between nodes of non-identical layers, wherein at least one of the arcs is between the group node and at least one of the plurality of observation nodes, wherein, the generated probabilistic model is configured to be invoked by reasoning engine for outputting probabilities of one or more conclusions or aggregation of the conclusions, in response to input observations, during a decision making process. 16. The method of claim 14 , further comprising assigning a value to each directed arc.