Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Yet the formalism can be straightforwardly integrated with exact methods whenever they are/become applicable. The approximations  use are controlled: they maintain consistently upper and lor bounds on the desired quantities at all times.  show that Boltzmann machines, sigmoid belief networks, or any combination (i.e., chain graphs) can be handled within the same framework. The accuracy of the methods is verified experimentally.
['Tommi S. Jaakkola', 'Michael I. Jordan']
I am writing a research paper on a idea Recursive Algorithms for Approximating Probabilities in Graphical Models
[{"from": "human", "value": "I am writing a research paper on a idea Recursive Algorithms for Approximating Probabilities in Graphical Models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Yet the formalism can be straightforwardly integrated with exact methods whenever they are/become applicable. The approximations  use are controlled: they maintain consistently upper and lor bounds on the desired quantities at all times.  show that Boltzmann machines, sigmoid belief networks, or any combination (i.e., chain graphs) can be handled within the same framework. The accuracy of the methods is verified experimentally."}]