Howtosignalprocessing
- What is covariance matrix in Gaussian process?
- What is a stationary covariance function?
- What is a covariance kernel?
- What is process in Gaussian process?
What is covariance matrix in Gaussian process?
The covariance matrix for a Gaussian process is a gram matrix obtained by evaluating some kernel function k(x,x′) pairwise between a set of observations. Because the hyperparameters (α,l,σ0) have no dependency on the index, x.
What is a stationary covariance function?
A stationary covariance function is a function of τ = x − x . Sometimes in this case we will write k as a function of a single argument, i.e. k(τ). The covariance function of a stationary process can be represented as the Fourier transform of a positive finite measure.
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′.
What is process in Gaussian process?
In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed.
