Semi-nonparametric Hermite-based densities with Monte Carlo maximum likelihood and convex initialization enable accurate non-Gaussian density and quantile estimation for nonlinear systems like the Lorenz attractor using far fewer samples than raw Monte Carlo.
On Estimation of a Probability Density Function and Mode
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Data-Efficient Non-Gaussian Semi-Nonparametric Density Estimation for Nonlinear Dynamical Systems
Semi-nonparametric Hermite-based densities with Monte Carlo maximum likelihood and convex initialization enable accurate non-Gaussian density and quantile estimation for nonlinear systems like the Lorenz attractor using far fewer samples than raw Monte Carlo.