Parameter estimation with maximal updated densities
نویسندگان
چکیده
A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i.e., irreducible) inputs parameters). The subsequent inferential target is distribution on parameters. Another type of quantify estimates “true” parameter values under the assumption that such should be reduced as more incorporated into problem, i.e., uncertainty considered epistemic. major contribution this work formulation and solution identification (PIP) within SIP. approach novel it utilizes forward (SFP) update an initial density only directions informed by data. In other words, method performs “selective regularization” not defined maximal updated (MUD) point defines PIP. significant full theory existence uniqueness MUD points linear maps with Gaussian distributions. Data-constructed Quantity Interest (QoI) also presented analyzed solving PIP means reducing estimate. We conclude demonstration general applicability two problems involving either spatial or temporal estimating uncertain first from stationary partial differential equation produce estimate boundary condition. second obtained state-of-the-art ADvanced CIRCulation (ADCIRC) obtain wind drag coefficients simulated extreme weather event near Shinnecock Inlet located Outer Barrier Long Island, NY, USA.
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ژورنال
عنوان ژورنال: Computer Methods in Applied Mechanics and Engineering
سال: 2023
ISSN: ['0045-7825', '1879-2138']
DOI: https://doi.org/10.1016/j.cma.2023.115906