Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  introduce two new algorithms for approximate inference of Bayesian networks that use edge deletion techniques. The first reduces a network to its maximal ight spanning tree using the Kullback-Leibler information divergence as edge ights, and then runs Pearl's algorithm on the resulting tree for linear-time inference. The second algorithm deletes edges from the triangulated graph until the biggest clique in the triangulated graph is below a desired bound, thus placing a polynomial time bound on inference. When tested for efficiency, these two algorithms perform up to 10,000 times faster than exact techniques. See www.cis.ksu.edu/~jas3466/research.html for more information.
['Julie Ann Thornton']
I am writing a research paper on a idea Approximate inference of Bayesian networks through edge deletion
[{"from": "human", "value": "I am writing a research paper on a idea Approximate inference of Bayesian networks through edge deletion"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  introduce two new algorithms for approximate inference of Bayesian networks that use edge deletion techniques. The first reduces a network to its maximal ight spanning tree using the Kullback-Leibler information divergence as edge ights, and then runs Pearl's algorithm on the resulting tree for linear-time inference. The second algorithm deletes edges from the triangulated graph until the biggest clique in the triangulated graph is below a desired bound, thus placing a polynomial time bound on inference. When tested for efficiency, these two algorithms perform up to 10,000 times faster than exact techniques. See www.cis.ksu.edu/~jas3466/research.html for more information."}]