GP posterior mean with Matern kernel

GP posterior mean with Matern kernel

Usage

morie_ghosal_gp_matern(
  x,
  y,
  nu = 1.5,
  length_scale = NULL,
  sigma_f = 1,
  noise = NULL,
  x_star = NULL
)

Arguments

x

Numeric vector or matrix of input points.

y

Numeric response vector.

nu

Matern smoothness parameter (default 1.5).

length_scale

Optional kernel length-scale.

sigma_f

Numeric signal sd (default 1).

noise

Optional observation noise sd.

x_star

Optional matrix of prediction points (defaults to x).

Value

Named list with estimate, se, mu, sd, length_scale, nu, noise, n, method.

Examples

set.seed(1)
morie_ghosal_gp_matern(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] 0.117538
#> 
#> $se
#> [1] 0.05578353
#> 
#> $mu
#>  [1]  6.666260e-01 -6.882583e-02  1.128560e+00 -1.774777e-01  2.226556e-01
#>  [6]  1.143902e+00  1.390457e-01 -1.467672e-01  4.990531e-01  4.571688e-01
#> [11]  9.392961e-01  6.493410e-02  6.440299e-01 -2.207877e-02 -1.880115e-01
#> [16] -5.814044e-01 -5.620659e-01  5.916519e-01 -1.523915e-01  4.868760e-01
#> [21]  4.546249e-01 -2.414643e-01 -1.820178e-01 -8.750506e-01  4.007311e-01
#> [26] -5.792237e-01 -3.369648e-01  7.157017e-02  3.657518e-01  1.574434e-02
#> [31] -2.360218e-02 -5.403801e-01  6.975076e-02 -5.798726e-01  5.019667e-01
#> [36]  4.850185e-01  5.182958e-01 -5.782362e-01  6.995771e-05 -2.259136e-01
#> [41] -2.826876e-01  3.243033e-01  8.290460e-02  4.636223e-01  9.379048e-01
#> [46]  9.965358e-01  1.257037e-01 -2.345075e-01 -5.204823e-01  1.780253e-01
#> 
#> $sd
#>  [1] 0.05387549 0.07420714 0.06768264 0.08508914 0.05629181 0.06244498
#>  [7] 0.05515988 0.04382719 0.04561083 0.05936374 0.07264873 0.04320209
#> [13] 0.05490503 0.09417685 0.06664266 0.03888276 0.04669191 0.05818622
#> [19] 0.04747640 0.04564659 0.05293403 0.04199558 0.06677995 0.09298367
#> [25] 0.04861154 0.03746450 0.04719505 0.08452609 0.06391561 0.04683134
#> [31] 0.08444034 0.04004270 0.04316437 0.03765350 0.08461972 0.05471731
#> [37] 0.05504036 0.03729581 0.06414708 0.04126523 0.04880000 0.05864554
#> [43] 0.05060225 0.04769662 0.05225063 0.05468577 0.04556139 0.04121017
#> [49] 0.04113033 0.05095799
#> 
#> $length_scale
#> [1] 0.7740596
#> 
#> $nu
#> [1] 1.5
#> 
#> $noise
#> [1] 0.09688279
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "GP regression (Matern kernel)"
#>