Listing 1: Complete Demo Program # synthetic_gpr.py # scikit Gaussian process regression on synthetic data # Anaconda3-2022.10 Python 3.9.13 # scikit 1.0.2 Windows 10/11 import numpy as np import ...

Python implementation of a regression model using Gaussian Process. It can be executed in a virtual environment (Conda). The learning process of the Gaussian Process Regression (GPR) will be shown as ...

Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.

The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian ...

we introduce a new Gaussian process regression stochastic volatility (GPRSV) model building procedures for financial time series data analysis and time-varying volatility modeling. The GPRSV extends ...

This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian framework, where the regression model is derived by assuming the model variables follow ...

Compared to other regression techniques, GPR is especially useful when there is limited training data. There are several tools and code libraries that you can use to create a GPR model. The ...