It has been shown that a class of probabilistic domain models cannot be learned correctly by several existing algorithms which employ a single-link lookahead search. When a multi-link lookahead search is used, the computational complexity of the learning algorithm increases. We study how to use parallelism to tackle the increased complexity. A parallel algorithm for learning belief networks is proposed. Our implementation in a parallel computer demonstrates the effectiveness of the algorithm.
belief network, learning, parallel computation.