Description Usage Arguments Value References
Computes P(min X ≤ Thresh) with choice of algorithm between ANMC_Gauss and MC_Gauss. The two most expensive parts are computed with the RCpp functions.
1 2 |
cBdg |
computational budget. |
Thresh |
threshold. |
mu |
mean vector. |
Sigma |
covariance matrix. |
E |
discretization design for the field. If |
q |
number of active dimensions. Can be passed either as an integer or as numeric vector of length 2. The vector is the range where to search for the best number of active dimensions. If |
pn |
coverage function vector. |
lightReturn |
boolean, if true light return. |
method |
method chosen to select the active dimensions. |
verb |
level of verbosity (0-5), selects verbosity also for ANMC_Gauss (verb-1) and MC_Gauss (verb-1). |
Algo |
choice of algorithm to compute the remainder Rq ("ANMC" or "MC"). |
A list containing
probability
: The probability estimate
variance
: the variance of the probability estimate
q
:the number of selected active dimensions
If lightReturn=F
then the list also contains:
aux_probabilities
: a list with the probability estimates: probability
the actual probability, pq
the biased estimator p_q, Rq
the conditional probability R_q
Eq
: the points of the design E selected for p_q
indQ
: the indices of the active dimensions chosen for p_q
resRq
: The list returned by the MC method used for R_q
Azzimonti, D. and Ginsbourger, D. (2016). Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Preprint at hal-01289126
Chevalier, C. (2013). Fast uncertainty reduction strategies relying on Gaussian process models. PhD thesis, University of Bern.
Dickmann, F. and Schweizer, N. (2014). Faster comparison of stopping times by nested conditional Monte Carlo. arXiv preprint arXiv:1402.0243.
Genz, A. (1992). Numerical computation of multivariate normal probabilities. Journal of Computational and Graphical Statistics, 1(2):141–149.
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