doc: gp_sample
Sample a Gaussian process in a hypercube and perform Bayesian inferance
Syntax
[f, Xs, Fs, Xt, Yt, Kss] = gp_sample(..., 'Name',Value)
Name-Value Pair Arguments
- d integer for the dimension (default: 1)
- size scalar for the length of the hypercube (default: 40)
- ns integer for the number of sampled points (default: 1000)
- nt integer for the number of training points (default: 10)
- kernel kernel function (default: @kernel_se_normiso)
- basis basis function (default: @basis_none)
- noise scalar for the noise standard deviation (default: 0.01)
- plot boolean to visualize the optimization (default: true)
- posterior boolean to compute the Bayesian inferance (default: true)
- verbose boolean to monitor the process (default: true)
Outputs
- f function which returns noisy observations
- Xs matrix (ns, d) of sampled data
- Fs matrix (ns, 1) of sampled values
- Xt matrix (nt, d) of training data
- Yt vector (nt, 1) of training observations
- Kss matrix (ns, ns) of kernel values