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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 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 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



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stk_pmisclass


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 STK_PMISCLASS computes the (expected) probability of misclassification

 CALL: P = stk_pmisclass (U, Z_PRED)

    computes the probability of misclassification P with respect to the
    threshold U using the posterior means and variances contained in Z_PRED,
    where Z_PRED is a dataframe with column names 'mean' and 'var' (as
    returned by stk_predict).

 CALL: P = stk_pmisclass (U, Z1_PRED, K12, K22)

    computes the expected probability of misclassification P with respect to
    the threshold U at a set of locations X1, assuming that observations are
    made at another set of location X2, where:

     * Z1_PRED contains, as above, the posterior means and variances at X1;

     * K12 is the posterior covariance matrix between the values of the process
       at X1 and the observations (at X2);

     * K22 is the posterior covariance matrix of the observations.

 See also: stk_predict, stk_example_doe04



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 STK_PMISCLASS computes the (expected) probability of misclassification





