This repository contains my learning journey with Gaussian Process Regression, organized into three main sections:

Directory Structure

01_statistics_foundations/

• Basic statistical concepts
• Bayesian statistics fundamentals
• Probability distributions

02_simulation_methods/

• Simulation-based inference
• Computational methods

03_gaussian_processes/

• GP fundamentals
• Simple GP implementations
• Medium complexity GPR models
• Advanced techniques (gradient-enhanced kriging)

Reference

Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (Vol. 2, No. 3, p. 4). Cambridge, MA: MIT press.

Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.