Abstract: A computer implemented method, computer program product and system for generating a Bayesian network. A dataset comprising multiple instances of multiple variables is received. A target variable from the received dataset is selected. Multiple parent sets of variables for the target variable are determined, such that, for each parent set of variables, the target variable is functionally dependent on the respective parent set of variables. For multiple variables of the received dataset, the selecting of a new target variable from the received dataset and determining multiple parent sets of variables for the new target variable is repeated. A Bayesian network (includes a directed acyclic graph of nodes and edges) is then generated for the variables such that one or more of the determined parent sets of variables for the target variables are inserted into the graph and edges from the graph are removed to ensure that the graph is acyclic.

Abstract: A computer implemented method, computer program product and system for generating a Bayesian network. A dataset comprising multiple instances of multiple variables is received. A target variable from the received dataset is selected. Multiple parent sets of variables for the target variable are determined, such that, for each parent set of variables, the target variable is functionally dependent on the respective parent set of variables. For multiple variables of the received dataset, the selecting of a new target variable from the received dataset and determining multiple parent sets of variables for the new target variable is repeated. A Bayesian network (includes a directed acyclic graph of nodes and edges) is then generated for the variables such that one or more of the determined parent sets of variables for the target variables are inserted into the graph and edges from the graph are removed to ensure that the graph is acyclic.

Abstract: A computer implemented method, computer program product and system for generating a Bayesian network. A dataset comprising multiple instances of multiple variables is received. A target variable from the received dataset is selected. Multiple parent sets of variables for the target variable are determined, such that, for each parent set of variables, the target variable is functionally dependent on the respective parent set of variables. For multiple variables of the received dataset, the selecting of a new target variable from the received dataset and determining multiple parent sets of variables for the new target variable is repeated. A Bayesian network (includes a directed acyclic graph of nodes and edges) is then generated for the variables such that one or more of the determined parent sets of variables for the target variables are inserted into the graph and edges from the graph are removed to ensure that the graph is acyclic.

Abstract: A computer implemented method, computer program product and system for generating a Bayesian network. A dataset comprising multiple instances of multiple variables is received. A target variable from the received dataset is selected. Multiple parent sets of variables for the target variable are determined, such that, for each parent set of variables, the target variable is functionally dependent on the respective parent set of variables. For multiple variables of the received dataset, the selecting of a new target variable from the received dataset and determining multiple parent sets of variables for the new target variable is repeated. A Bayesian network (includes a directed acyclic graph of nodes and edges) is then generated for the variables such that one or more of the determined parent sets of variables for the target variables are inserted into the graph and edges from the graph are removed to ensure that the graph is acyclic.