Calculate a GP log likelihood
Calculate a Gaussian process (GP) log likelihood or posterior probability with reference to a C-side GP object
llikGP(gpi, dab = c(0, 0), gab = c(0, 0)) llikGPsep(gpsepi, dab = c(0, 0), gab = c(0, 0))
gpi |
a C-side GP object identifier (positive integer);
e.g., as returned by |
gpsepi |
similar to |
dab |
|
gab |
|
An “ab” parameter is a non-negative 2-vector describing
shape and rate parameters to a Gamma prior; a zero-setting for
either value results in no-prior being used in which case a log likelihood
is returned. If both ab parameters are specified, then the value
returned can be interpreted as a log posterior density. See darg
for more information about ab
A real-valued scalar is returned.
Robert B. Gramacy rbg@vt.edu
## partly following the example in mleGP library(MASS) ## motorcycle data and predictive locations X <- matrix(mcycle[,1], ncol=1) Z <- mcycle[,2] ## get sensible ranges d <- darg(NULL, X) g <- garg(list(mle=TRUE), Z) ## initialize the model gpi <- newGP(X, Z, d=d$start, g=g$start) ## calculate log likelihood llikGP(gpi) ## calculate posterior probability llikGP(gpi, d$ab, g$ab) ## clean up deleteGP(gpi)
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