@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00984515v1> a dcat:Dataset ;
    dct:description """
              Gaussian random fields (GRF) conditional simulation is a key ingredient in many spatial statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on non-linear functionals of GRFs conditional on data. Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points. Here we study the settings where conditioning observations are assimilated batch-sequentially, i.e. one point or batch of points at each stage. Assuming that conditional simulations have been performed at a previous stage, we aim at taking advantage of already available sample paths and by-products in order to produce updated conditional simulations at minimal cost. We provide explicit formulas allowing to update an ensemble of sample paths conditioned on $n\\geq 0$ observations to an ensemble conditioned on $n+q$ observations, for arbitrary $q\\geq 1$. Compared to direct approaches, the proposed formulas prove to substantially reduce computational complexity. Moreover, these formulas enable explicitly exhibiting how the $q$ ''new'' observations are updating the ''old'' sample paths. Detailed complexity calculations highlighting the benefits of our approach with respect to state-of-the-art algorithms are provided and are complemented by numerical experiments.
            """ ;
    dct:identifier "hal-00984515" ;
    dct:issued "2026-05-05T12:51:51.620021"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-05T12:51:51.620026"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Fast Update of Conditional Simulation Ensembles" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00984515v1/resource/2fa41049-438a-4e5f-a23f-bdc38dda585d> ;
    dcat:keyword "batch-sequential-strategies",
        "gaussian-random-fields",
        "infoeu-reposemanticspreprint",
        "infoinfo-mocomputer-science-csmodeling-and-simulation",
        "kriging-residual-algorithm",
        "kriging-update-equations",
        "mathmath-stmathematics-mathstatistics-mathst",
        "physphysphys-geo-phphysics-physicsphysics-physicsgeophysics-physicsgeo-ph",
        "preprints-working-papers-",
        "sdemcgenvironmental-sciencesglobal-changes",
        "sdustugpsciences-of-the-universe-physicsearth-sciencesgeophysics-physicsgeo-ph",
        "statapstatistics-statapplications-statap",
        "statmestatistics-statmethodology-statme",
        "statmlstatistics-statmachine-learning-statml",
        "statthstatistics-statstatistics-theory-statth" ;
    dcat:landingPage <https://hal.science/hal-00984515> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00984515v1/resource/2fa41049-438a-4e5f-a23f-bdc38dda585d> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-05T12:51:51.622955"^^xsd:dateTime ;
    dct:modified "2026-05-05T12:51:51.601675"^^xsd:dateTime ;
    dct:title "Fast Update of Conditional Simulation Ensembles" ;
    dcat:accessURL <https://hal.science/hal-00984515> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://hal.science/hal-00984515> a foaf:Document .