Hi,

I have a sample of size 100 from a function in interval [0,1] which can be
assumed to come from a scalar-valued stationary Gaussian process. There are
about 500 observation points in the interval. I need an effective and fast
way to simulate from the Gaussian process conditioned on the available data.
I can of course estimate the mean and  500x500 covariance matrix from data.
I have searched both the Rsite and Internet in general but have not found
any function or program which could work directly for this type of data.
There are some packages in R to simulate from Gaussian processes(e.g. geoR,
waveslim and spectralGP) but I couldn't figure out if any function in these
packages could work in my framework.

Given the size of covariance matrix I was thinking about using SVD to reduce
the size but I would like to hear from the list members if there are more
effective ways of doing this.

/Mikeal

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