Howtosignalprocessing
- What is a covariance kernel?
- How do I choose a GPR kernel?
- How do I choose a kernel function?
- What does kernel do in Gaussian process?
What is a covariance kernel?
In loose terms, a kernel or covariance function k(x,x′) specifies the statistical relationship between two points x,x′ in your input space; that is, how markedly a change in the value of the Gaussian Process (GP) at x correlates with a change in the GP at x′.
How do I choose a GPR kernel?
Here's a good way that you might justify a kernel choice in a report. First - fit 2 or 3 different kernel functions that you might think are reasonable. Second -calculate test statistics of interest such as sample autocovariance at different distances on the original data.
How do I choose a kernel function?
Always try the linear kernel first, simply because it's so much faster and can yield great results in many cases (specifically high dimensional problems). If the linear kernel fails, in general your best bet is an RBF kernel. They are known to perform very well on a large variety of problems.
What does kernel do in Gaussian process?
The kernel function k(xₙ, xₘ) used in a Gaussian process model is its very heart — the kernel function essentially tells the model how similar two data points (xₙ, xₘ) are. Several kernel functions are available for use with different types of data, and we will take a look at a few of them in this section.
