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stk_conditioning


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 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



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 STK_CONDITIONING produces conditioned sample paths



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stk_generate_samplepaths


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 STK_GENERATE_SAMPLEPATHS generates sample paths of a Gaussian process

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XT)

    generates one sample path ZSIM, using the kriging model MODEL and the
    evaluation points XT. Both XT and ZSIM are structures, whose field 'a'
    contains the actual numerical values.

 CALL: ZSIM = stk_generate_samplepaths (MODEL, XT, NB_PATHS)

    generates NB_PATHS sample paths at once.

 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 will be an stk_dataframe if at least one of the
    following conditions is met:

      a) the MODEL structure has a non-empty char field named 'response_name';

      b) one of the input arguments XT, XI or ZI is an stk_dataframe object.

    If both MODEL.response_name and ZI.colnames exist and are non-empty, they
    must be equal (if they are not, ZSIM.colnames is empty).

 EXAMPLES: see stk_example_kb05, stk_example_kb07

 See also stk_conditioning, stk_cholcov



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 STK_GENERATE_SAMPLEPATHS generates sample paths of a Gaussian process





