Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: State-space models have been successfully used for more than fifty years in different areas of science and engineering.  present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models,  offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo.  also present stochastic variational inference and online learning approaches for fast learning with long time series.
['Roger Frigola', 'Yutian Chen', 'Carl Edward Rasmussen']
I am writing a research paper on a idea Variational Gaussian Process State-Space Models
[{"from": "human", "value": "I am writing a research paper on a idea Variational Gaussian Process State-Space Models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: State-space models have been successfully used for more than fifty years in different areas of science and engineering.  present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models,  offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo.  also present stochastic variational inference and online learning approaches for fast learning with long time series."}]