smoothDE: a smooth density estimator with good performance

Probability density estimation is the problem of inferring an underlying probability from a sampling of points. This study introduces smoothDE, an algorithm that uses Bayesian Field Theory to optimize non-parametric density estimation. smoothDE deterministically finds an optimal density function based on the probability of observed data, subject to a smoothing constraint and its associated prior probability. smoothDE's predicted densities have almost universally lower Kullback-Leibler divergences from simulated Gaussian Mixtures densities when compared to similar Bayesian Field Theory methods and Kernel Density Estimators. smoothDE was even able to outperform a specialized Bayesian Gaussian Mixture density estimator at lower samplings. smoothDE's ability to quickly fit arbitrary densities allowed it to be used as a preprocessing step for classification algorithm, in certain cases boosting classifier performance.