logLikelihoodFun.NoiseKriging

logLikelihoodFun,NoiseKriging,NoiseKriging-method

&lt;-

function(x)

(sin(12

cos(7

0.7)

set.seed(123)

as.matrix(runif(10))

f(X)

X/10

*rnorm(nrow(X))

NoiseKriging(y,

(X/10)^2,

kernel

"matern3_2")

print(k)

theta0

k$theta()

ll_sigma2

function(sigma2)

logLikelihoodFun(k,

cbind(theta0,sigma2))$logLikelihood

seq(from

0.001,

length.out

101)

plot(s2,

Vectorize(ll_sigma2)(s2),

type

'l')

abline(v

k$sigma2(),

"blue")

sigma20

k$sigma2()

ll_theta

function(theta)

cbind(theta,sigma20))$logLikelihood

plot(t,

Vectorize(ll_theta)(t),

k$theta(),

function(theta_sigma2)

theta_sigma2)$logLikelihood

contour(t,s2,matrix(ncol=length(s2),ll(expand.grid(t,s2))),xlab="theta",ylab="sigma2")

points(k$theta(),k$sigma2(),col='blue')

Compute Log-Likelihood of NoiseKriging Model

Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should
provide most standard Kriging / Gaussian process regression features
(like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages).
'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson,
and 'lbfgsb_cpp' is a 'C++' wrapper by Colin Fang around 'lbfgsb' library (BSD-3 license) by
Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.

Yann Richet

rlibkriging

Kriging Models using the 'libKriging' Library

Pascal Havé

Yves Deville

Conrad Sanderson

Colin Fang

Ciyou Zhu

Richard Byrd

Jorge Nocedal

Jose Luis Morales

logLikelihoodFun.NoiseKriging function

<dl><dt>object</dt>
<dd>An S3 NoiseKriging object.</dd>
<dt>theta_sigma2</dt>
<dd>A numeric vector of (positive) range parameters and variance at
which the log-likelihood will be evaluated.</dd>
<dt>grad</dt>
<dd>Logical. Should the function return the gradient?</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Yann Richet <a href="/link/yann.richet%40irsn.fr?package=rlibkriging&version=0.7-4.3" data-mini-rdoc="rlibkriging::yann.richet@irsn.fr">yann.richet@irsn.fr</a>

Author

Compute Log-Likelihood of NoiseKriging Model — logLikelihoodFun.NoiseKriging

<dl>

<dt>object</dt>
<dd>An S3 NoiseKriging object.</dd>


<dt>theta_sigma2</dt>
<dd>A numeric vector of (positive) range parameters and variance at
which the log-likelihood will be evaluated.</dd>


<dt>grad</dt>
<dd>Logical. Should the function return the gradient?</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

Yann Richet <a href='mailto:yann.richet@irsn.fr'>yann.richet@irsn.fr</a>

Compute Log-Likelihood of NoiseKriging Model

logLikelihoodFun.NoiseKriging: Compute Log-Likelihood of NoiseKriging Model

Description

Usage

Value

Arguments

Author