Application of Quantum Theory to Super-parametric Density Estimation

TL;DR

This paper introduces a novel super-parametric density estimation method leveraging quantum theory principles, combining elementary calculus with nonlinear optimization to improve nonparametric density estimators.

Contribution

It presents a new approach to density estimation using quantum probability amplitudes, providing a constructive solution to complex nonlinear optimization problems.

Findings

Establishment of a complete super-parametric density estimation framework

Development of a simple, comprehensive optimization procedure

Numerical results demonstrating the effectiveness of the approach

Abstract

In this paper, we will discuss how to generalize nonparametric density estimators to MLE parametric estimators. Basing on the Parzen window theory and using the advantages of probability amplitude of quantum theory, we model a nonlinear optimization problem and it is very difficult, if not impossible, to solve the problem. A constructive procedure for solving the nonlinear programming problem is studied. Though it seems to be very complicated, the approach of this paper is simple and comprehensive. More precisely, the lemmas, the theorems and their proofs serve the purpose for mathematical rigor and practical computation. Instead of using techniques and terminologies of advanced mathematics, we use the popular techniques and terminologies of elementary calculus. From the numerical results of the paper by Y. --S. Tsai et al. [7], it shows that a new approach of density estimation, super-parametric density estimation, is established completely. Strictly speaking, the work of the paper is not confined in the category of statistics. It could be classified into nonlinear analysis such as optimization on linear space, or manifold, and the algorithm of computer science.