GP posterior mean with squared-exponential kernel

GP posterior mean with squared-exponential kernel

Usage

morie_ghosal_gp_squared_exponential(
  x,
  y,
  length_scale = NULL,
  sigma_f = 1,
  noise = NULL,
  x_star = NULL
)

Arguments

x

Numeric vector or matrix of input points.

y

Numeric response vector.

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, noise, n, method.

Examples

set.seed(1)
morie_ghosal_gp_squared_exponential(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] 0.1170772
#> 
#> $se
#> [1] 0.03487577
#> 
#> $mu
#>  [1]  0.80390118 -0.20399032  1.20217771  0.25887980 -0.04464533  1.18485493
#>  [7]  0.10509424  0.17369597  0.15549033 -0.02250289  0.20168695  0.01934957
#> [13]  0.79061893 -0.17392582  0.05468760 -0.31921145 -0.32112680  0.10588530
#> [19]  0.15302918  0.16221678  0.11587455  0.16461122 -0.29236834 -0.67174490
#> [25]  0.16968374 -0.31688732 -0.25494405  0.07578198  0.40370783  0.04674204
#> [31]  0.10502807 -0.29727329  0.01715742 -0.31744398  0.38584683  0.23513475
#> [37]  0.18233809 -0.31606077  0.05801875  0.16911315 -0.24590275 -0.12128079
#> [43]  0.17751476  0.14704263  0.95312345  0.99386451 -0.00672778  0.16792436
#> [49] -0.29123316  0.13105517
#> 
#> $sd
#>  [1] 0.03329001 0.02882010 0.04411942 0.07236252 0.02888946 0.04307109
#>  [7] 0.02709173 0.02590847 0.02600087 0.02867464 0.05605108 0.02835028
#> [13] 0.03314600 0.08640793 0.04256209 0.02647477 0.02666642 0.03257666
#> [19] 0.02758870 0.02583243 0.03135294 0.02662205 0.02765663 0.07396358
#> [25] 0.02564364 0.02642408 0.02665284 0.06580259 0.03081810 0.02801917
#> [31] 0.04886122 0.02637643 0.02837411 0.02643342 0.06363126 0.03015074
#> [37] 0.02990941 0.02641231 0.04125640 0.02626646 0.02672981 0.02787028
#> [43] 0.02556680 0.02620533 0.03550223 0.03635348 0.02860875 0.02635956
#> [49] 0.02640063 0.02967948
#> 
#> $length_scale
#> [1] 0.7740596
#> 
#> $noise
#> [1] 0.09688279
#> 
#> $n
#> [1] 50
#> 
#> $method
#> [1] "GP regression (squared-exponential kernel)"
#>