Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the marginal likelihood of the data given a candidate structure. This term can be computed in closed-form for standard parametric families (e.g., Gaussians), and can be approximated, at some computational cost, for some semi-parametric families (e.g., mixtures of Gaussians).#R##N##R##N# present a new family of continuous variable probabilistic networks that are based on Gaussian Process priors. These priors are semiparametric in nature and can learn almost arbitrary noisy functional relations. Using these priors,  can directly compute marginal likelihoods for structure learning. The resulting method can discover a wide range of functional dependencies in multivariate data.  develop the Bayesian score of Gaussian Process Networks and describe how to learn them from data.  present empirical results on artificial data as ll as on real-life domains with non-linear dependencies.
['Nir Friedman', 'Iftach Nachman']
I am writing a research paper on a idea Gaussian Process Networks
[{"from": "human", "value": "I am writing a research paper on a idea Gaussian Process Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the marginal likelihood of the data given a candidate structure. This term can be computed in closed-form for standard parametric families (e.g., Gaussians), and can be approximated, at some computational cost, for some semi-parametric families (e.g., mixtures of Gaussians).#R##N##R##N# present a new family of continuous variable probabilistic networks that are based on Gaussian Process priors. These priors are semiparametric in nature and can learn almost arbitrary noisy functional relations. Using these priors,  can directly compute marginal likelihoods for structure learning. The resulting method can discover a wide range of functional dependencies in multivariate data.  develop the Bayesian score of Gaussian Process Networks and describe how to learn them from data.  present empirical results on artificial data as ll as on real-life domains with non-linear dependencies."}]