Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

Abstract

We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e., duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naïve approach, utilizing one random sample from the input distribution (i.e., disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real-time applications.

AI Raman spectroscopy through machine learning

“…The Bayesian works by specifying a prior distribution. 8 The updated distribution through the Bayesian optimization called the posterior distribution incorporates information from both the prior distribution and the dataset. To get predictions at unseen points of interest, the predictive distribution can be calculated by weighting all possible predictions by their calculated posterior distribution.…”

“…8 Using that assumption and solving for the predictive distribution, a Gaussian distribution is created, from which a point prediction using its mean and an uncertainty quantification using its variance can be obtained. 8 Before training the model the data from the Raman measurements were organized into three categories, first being the training set which contains the response variable which is the result of desired for prediction while training the model. In this case a 1 molar (M) urea concentration was chosen as the response variable for identification of urea in different mixtures.…”

Classification of cassava leaf diseases using deep Gaussian transfer learning model

“…11,12 Gaussian process models are resistant to overfitting and can quantify the uncertainty of predictions, in contrast to many supervised machine learning techniques like least squares and artificial neural networks (ANNs). 13 Although Gaussian process classifiers perform similarly to non-linear support vector machines (SVMs), they have been preferred due to additional benefits such as uncertainty representation and hyper-parameter selection. 14 The accuracy of Gaussian processes (GPs) is compromised by their inductive biases even if they offer uncertainty estimates and a marginal likelihood target.…”

“…Particularly for problems with small data sets, Gaussian processes (GPs) can be helpful 11,12 . Gaussian process models are resistant to overfitting and can quantify the uncertainty of predictions, in contrast to many supervised machine learning techniques like least squares and artificial neural networks (ANNs) 13 . Although Gaussian process classifiers perform similarly to non‐linear support vector machines (SVMs), they have been preferred due to additional benefits such as uncertainty representation and hyper‐parameter selection 14 .…”