 The same idea of using a DAG to represent a family of paths occurs in the binary decision diagram , a DAG @-@ based data structure for representing binary functions . In a binary decision diagram , each non @-@ sink vertex is labeled by the name of a binary variable , and each sink and each edge is labeled by a 0 or 1 . The function value for any truth assignment to the variables is the value at the sink found by following a path , starting from the single source vertex , that at each non @-@ sink vertex follows the outgoing edge labeled with the value of that vertex 's variable . Just as directed acyclic word graphs can be viewed as a compressed form of tries , binary decision diagrams can be viewed as compressed forms of decision trees that save space by allowing paths to rejoin when they agree on the results of all remaining decisions . 
 The same idea of using a DAG to represent a family of paths <a href='uninflect' title='1'><mark>occurs</mark></a> in the binary decision diagram , a DAG @-@ based data structure for representing binary functions . In a binary decision diagram , each non @-@ sink vertex is labeled by the name of a binary variable , and each sink and each edge is labeled by a 0 or 1 . The function value for any truth assignment to the variables is the value at the sink found by following a path , starting from the single source vertex , that at each non @-@ sink vertex <a href='uninflect' title='2'><mark>follows</mark></a> the outgoing edge labeled with the value of that vertex <a href='null_genetive' title='3'><mark>'s</mark></a>variable . Just as directed acyclic word graphs can be viewed as a compressed form of tries , binary decision diagrams can be viewed as compressed forms of decision trees <a href='null_relcl' title='4'><mark>that</mark></a>save space by allowing paths to rejoin when they agree on the results of all remaining decisions . 
The same idea of using a DAG to represent a fam of paths occur in the binary decision diagram , a DAG @-@ based data structure for representing binary functions . In a binary decision diagram , each non @-@ sink vertex is labeled by the name of a binary variable , and each sink and each edge is labeled by a 0 or 1 . The function value for any truth assignment to da variables is the value at tdasink found by following a path , starting from the single source vertex , that at each non @-@ sink vertex follow the outgoing edge labeled wid thdaalue of that vertex  variable . Just as directed acyclic word graphs can b viewed as a compressed form of tries , binary decision diagrams can be viewed as compressed forms of decision trees save space by allowing paths to rejoin when they agree on the results of all remaining decisions .