# Parameter Estimation with Data-Driven Nonparametric Likelihood Functions

@article{Jiang2019ParameterEW, title={Parameter Estimation with Data-Driven Nonparametric Likelihood Functions}, author={Shixiao W. Jiang and John Harlim}, journal={Entropy}, year={2019}, volume={21} }

In this paper, we consider a surrogate modeling approach using a data-driven nonparametric likelihood function constructed on a manifold on which the data lie (or to which they are close). The proposed method represents the likelihood function using a spectral expansion formulation known as the kernel embedding of the conditional distribution. To respect the geometry of the data, we employ this spectral expansion using a set of data-driven basis functions obtained from the diffusion maps…

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