Approximating a Gaussian Process

1. What are Gaussian process approximation methods?
2. What is local approximate Gaussian process?
3. What is a Gaussian process simple explanation?
4. What is Gaussian process optimization?

What are Gaussian process approximation methods?

In statistics and machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly likelihood evaluation and prediction.

What is local approximate Gaussian process?

Local approximate Gaussian process models

The methods in the laGP package take a two-pronged approach to large data GP regression. They (1) leverage sparsity, but in fact only work with small dense matrices. And (2) the many-independent nature of calculations facilitates massive parallelization.

What is a Gaussian process simple explanation?

Gaussian Process is a machine learning technique. You can use it to do regression, classification, among many other things. Being a Bayesian method, Gaussian Process makes predictions with uncertainty. For example, it will predict that tomorrow's stock price is $100, with a standard deviation of $30.

What is Gaussian process optimization?

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.