Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

TL;DR

This paper introduces a Bayesian-based method using exponential cubic spline smoothing to accurately estimate profile gradients from noisy data, outperforming existing techniques.

Contribution

The paper presents a novel Bayesian framework for gradient estimation that effectively handles noise and improves accuracy over current methods.

Findings

Outperforms state-of-the-art gradient estimation methods

Effective in high-noise scenarios

Quantified accuracy through comprehensive synthetic data experiments

Abstract

Attaining reliable profile gradients is of utmost relevance for many physical systems. In most situations, the estimation of gradient can be inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the profile gradient by taking the subsequent analytic derivative. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. The ill-posedness gets worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the profile gradient of discrete noisy data is presented. The method is developed within a Bayesian framework. Comprehensive numerical experiments are conducted on synthetic data at different levels of random noise. The accuracy of the proposed method is quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.