GPR MATLAB: Gaussian Process Regression Implementation and Analysis Toolkit

GPR MATLAB This function is designed to work with MATLAB version 5 and above. Please feel free to contact us with any suggestions for improvement or to report any bugs you encounter. The function includes the following features: - Data fitting using Gaussian Process Regression (GPR) with implementation of kernel functions including squared exponential, Matern, and rational quadratic kernels. The code handles hyperparameter optimization through marginal likelihood maximization. - Advanced plotting capabilities for data visualization, including predictive mean plots, confidence intervals, and covariance structure visualization. The plotting module supports 2D and 3D data representation with customizable display parameters. - Computation of essential statistical metrics for data analysis, including predictive variances, log marginal likelihood, and model evidence calculations. The statistical module incorporates efficient matrix operations for large-scale data processing. - User-friendly interface with intuitive function calls and parameter settings. The implementation features input validation, error handling, and comprehensive documentation within the code comments. This function is expected to be useful in various fields such as geophysics, environmental science, and medical research. Future versions will include additional features and improvements, such as sparse GPR implementations for large datasets and support for additional covariance structures.