Gaussian Process Regression (GPR)
Gaussian Process Regression (GPR) is a powerful and flexible non-parametric regression technique used in machine learning and statistics. It is particularly useful when dealing with problems involving continuous data, where the relationship between input variables and output is not explicitly known or can be complex. GPR is a Bayesian approach that can model certainty in predictions, making it a valuable tool for various applications, including optimization, time series forecasting, and more. GPR is based on the concept of a Gaussian process, which is a collection of random variables, any finite number of which have a joint Gaussian distribution. A Gaussian process can be thought of as a distribution of functions.
Gaussian Process Regression (GPR)
Regression and probabilistic classification issues can be resolved using the Gaussian process (GP), a supervised learning technique. Since each Gaussian process can be thought of as an infinite-dimensional generalization of multivariate Gaussian distributions, the term “Gaussian” appears in the name. We will discuss Gaussian processes for regression in this post, which is also referred to as Gaussian process regression (GPR). Numerous real-world issues in the fields of materials science, chemistry, physics, and biology have been resolved with the use of GPR.
Table of Content
- Gaussian Process Regression (GPR)
- Key Concepts of Gaussian Process Regression (GPR)
- Mathematical Concept of Gaussian Process Regression (GPR)
- Implementation of Gaussian Process in Python