Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies beten stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with specified in-degrees. Another class additionally requires that the graph is connected. For both classes,  propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity.  also propose algorithms to identify the r-best approximations among these classes, enabling robust decision making.
['Christopher J. Quinn', 'Ali Pinar', 'Negar Kiyavash']
I am writing a research paper on a idea Bounded Degree Approximations of Stochastic Networks
[{"from": "human", "value": "I am writing a research paper on a idea Bounded Degree Approximations of Stochastic Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies beten stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with specified in-degrees. Another class additionally requires that the graph is connected. For both classes,  propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity.  also propose algorithms to identify the r-best approximations among these classes, enabling robust decision making."}]