STK_CONDITIONING produces conditioned sample paths CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM, XI_IND) produces conditioned sample paths ZSMIC from the unconditioned sample paths ZSIM, using the matrix of kriging weights LAMBDA. Conditioning is done with respect to a finite number NI of observations, located at the indices given in XI_IND (vector of length NI), with corresponding noiseless observed values ZI. The matrix LAMBDA must be of size NI x N, where N is the number of evaluation points for the sample paths; such a matrix is typically provided by stk_predict(). Both ZSIM and ZSIMC have size N x NB_PATHS, where NB_PATH is the number sample paths to be dealt with. ZI is a column of length NI. CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM) assumes that the oberved values ZI correspond to the first NI evaluation points. CALL: ZSIMC = stk_conditioning (LAMBDA, ZI, ZSIM, XI_IND, NOISE_SIM) produces conditioned sample paths ZSMIC from the unconditioned sample paths ZSIM, using the matrix of kriging weights LAMBDA. Conditioning is done with respect to a finite number NI of observations, located at the indices given in XI_IND (vector of length NI), with corresponding noisy observed values ZI, using a NI x N matrix NOISE_SIM of simulated noise values. NOTE: Conditioning by kriging stk_conditioning uses the technique called "conditioning by kriging" (see, e.g., Chiles and Delfiner, Geostatistics: Modeling Spatial Uncertainty, Wiley, 1999) NOTE: Output type The output argument ZSIMC will be an stk_dataframe if either LAMBDA or ZSIM are stk_dataframe. In case of conflicting row names (coming from ZSIM.rownames on the one hand and LAMBDA.colnames on the other hand), ZSIMC.rownames is {}. EXAMPLE: stk_example_kb05 See also stk_generate_samplepaths, stk_predict
Package: stk