# estimators: Return Maximum A Posteriori (MAP) and Mean A Posteriori... In mathieucarmassi/CaliCo: Code Calibration in a Bayesian Framework

## Description

`estimators` is a function that returns a list of two elements which are the MAP and the Mean A Posteriori

## Usage

 `1` ```estimators(modelfit) ```

## Arguments

 `modelfit` a `calibrate.class` object

## Value

return a `list`:

• MAP The Maximum A Posteriori

• MEAN The Mean A Posteriori

M. Carmassi

## 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``` ```## Not run: ###################### The code to calibrate X <- cbind(seq(0,1,length.out=10),seq(0,1,length.out=10)) code <- function(X,theta) { return((6*X[,1]*theta[2]-2)^2*theta[1]*sin(theta[3]*X[,2]-4)) } Yexp <- code(X,c(1,1,11))+rnorm(10,0,0.1) ############### For the first model ###### Definition of the model md <- model(code,X,Yexp,"model1") ###### Definition of the prior densities 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=600,thetaInit=c(1,1,11,0.1),r=c(0.3,0.3), sig=diag(4),Nchains=1,burnIn=100) ###### Run the calibration mdfit <- calibrate(md,pr,opt.estim) estimators(mdfit) ## End(Not run) ```

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