bayes {PEIP} | R Documentation |
Bayes Inversion
Description
Given a linear inverse problem Gm=d, a prior mean mprior and covariance matrix covm, data d, and data covariance matrix covd, this function computes the MAP solution and the corresponding covariance matrix.
Usage
bayes(G, mprior, covm, d, covd)
Arguments
G |
Design Matrix |
mprior |
vector, prior model |
covm |
vector, model covariance |
d |
vector, right hand side |
covd |
vector, data covariance |
Value
vector model
Author(s)
Jonathan M. Lees<jonathan.lees@unc.edu>
References
Aster, R.C., C.H. Thurber, and B. Borchers, Parameter Estimation and Inverse Problems, Elsevier Academic Press, Amsterdam, 2005.
Examples
## Not run:
set.seed(2015)
G = setDesignG()
###
mtruem=matrix(rep(0, 16*16), ncol=16,nrow=16);
mtruem[9,9]=1; mtruem[9,10]=1; mtruem[9,11]=1;
mtruem[10,9]=1; mtruem[10,11]=1;
mtruem[11,9]=1; mtruem[11,10]=1; mtruem[11,11]=1;
mtruem[2,3]=1; mtruem[2,4]=1;
mtruem[3,3]=1; mtruem[3,4]=1;
###
mtruev=as.vector(mtruem);
imagesc(matrix(mtruem,16,16) , asp=1 , main="True Model" );
matrix(mtruem,16,16) , asp=1 , main="True Model" )
###
dtrue=G %*% mtruev;
###
d=dtrue+0.01*rnorm(length(dtrue));
covd = 0.1*diag( nrow=length(d) )
covm = 1*diag( nrow=dim(G)[2] )
## End(Not run)
[Package PEIP version 2.2-5 Index]