STK_GENERATE_SAMPLEPATHS generates sample paths of a Gaussian process CALL: ZSIM = stk_generate_samplepaths (MODEL, XT) generates one sample path ZSIM of the Gaussian process MODEL discretized on the evaluation points XT. The input argument XT can be either a numerical matrix or a dataframe. The output argument ZSIM has the same number of rows as XT. More precisely, on a factor space of dimension DIM, * XT must have size NS x DIM, * ZSIM will have size NS x 1, where NS is the number of simulation points. Note that, in the case where MODEL is a model for noisy observations, this function simulates sample paths of the underlying (latent) Gaussian process, i.e., noiseless observations. CALL: ZSIM = stk_generate_samplepaths (MODEL, XT, NB_PATHS) generates NB_PATHS sample paths at once. In this case, the output argument ZSIM has size NS x NB_PATHS. CALL: ZSIM = stk_generate_samplepaths (MODEL, XI, ZI, XT) generates one sample path ZSIM, using the kriging model MODEL and the evaluation points XT, conditional on the evaluations (XI, ZI). CALL: ZSIM = stk_generate_samplepaths (MODEL, XI, ZI, XT, NB_PATHS) generates NB_PATHS conditional sample paths at once. NOTE: Sample size limitation This function generates (discretized) sample paths using a Cholesky factorization of the covariance matrix, and is therefore restricted to moderate values of the number of evaluation points. NOTE: Output type The output argument ZSIM is a plain (double precision) numerical array, even if XT is a data frame. Row names can be added afterwards as follows: ZSIM = stk_generate_samplepaths (MODEL, XT); ZSIM = stk_dataframe (ZSIM, {}, XT.rownames); EXAMPLES: see stk_example_kb05, stk_example_kb07 See also stk_conditioning, stk_cholcov
Package: stk