# sequentialDesign: Calibration with a sequential design In mathieucarmassi/CaliCo: Code Calibration in a Bayesian Framework

## Description

The aim is to reduce the error produced by the initial estimation of the Gaussian process by fortifying the initial DOE. The method consists in proposing new points based on the expectancy improvement criterion. The method and the algorithm are detailed in [Damblin et al. 2018]

## Usage

 `1` ```sequentialDesign(md, pr, opt.estim, k) ```

## Arguments

 `md` the model to improve (model2 or model4) `pr` list of priors to use for calibration `opt.estim` estimation options NgibbsNumber of iteration of the algorithm Metropolis within Gibbs Nmh Number of iteration of the Metropolis Hastings algorithm thetaInit Initial point r regulation percentage in the modification of the k in the Metropolis Hastings sig Covariance matrix for the proposition distribution (k*sig) Nchains Number of MCMC chains to run (if Nchain>1 an output is created called mcmc which is a coda object `codamenu`) burnIn Number of iteration to withdraw `k` number of iteration in the algorithm

## Value

a `seqDesign.class`

## References

DAMBLIN, Guillaume, BARBILLON, Pierre, KELLER, Merlin, et al. Adaptive numerical designs for the calibration of computer codes. SIAM/ASA Journal on Uncertainty Quantification, 2018, vol. 6, no 1, p. 151-179.

## See Also

`model`, `prior`, `calibrate`, `sequentialDesign`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```## Not run: ###### The code to calibrate X <- cbind(seq(0,1,length.out=5),seq(0,1,length.out=5)) code <- function(X,theta) { return((6*X[,1]*theta-2)^2*theta*sin(theta*X[,2]-4)) } Yexp <- code(X,c(1,1,11))+rnorm(5,0,0.1) ###### For the second model ### code function is available, no DOE generated upstream binf <- c(0.9,0.9,10.5) bsup <- c(1.1,1.1,11.5) opt.gp <- list(type="matern5_2", DOE=NULL) opt.emul <- list(p=3,n.emul=150,binf=binf,bsup=bsup,type="maximinLHS") model2 <- model(code,X,Yexp,"model2", opt.gp=opt.gp, opt.emul=opt.emul) model2 %<% list(theta=c(1,1,11),var=0.1) pr <- prior(type.prior=c("gaussian","gaussian","gaussian","gamma"),opt.prior= list(c(1,0.01),c(1,0.01),c(11,3),c(2,0.1))) ###### Definition of the calibration options opt.estim=list(Ngibbs=200,Nmh=400,thetaInit=c(1,1,11,0.1),r=c(0.3,0.3), sig=diag(4),Nchains=1,burnIn=100) ###### Run the sequential calibration mdfit <- sequentialDesign(model2,pr,opt.estim,2) #plot(mdfit,X[,1]) ## End(Not run) ```

mathieucarmassi/CaliCo documentation built on Aug. 14, 2019, 11:32 a.m.