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Markov chain Monte Carlo for a hyperbolic Bayesian inverse problem in traffic flow modeling

• Authors : Jeremie Coullon; Yvo Pokern

Subjects: Bayesian inverse problem; MCMC; motorway traffic flow

• Source: Data-Centric Engineering, Vol 3 (2022)

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MCMC for a hyperbolic Bayesian inverse problem in traffic flow modelling – ADDENDUM

• Authors : Jeremie Coullon; Yvo Pokern

Subjects: Engineering (General). Civil engineering (General); TA1-2040

• Source: Data-Centric Engineering, Vol 3 (2022)

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CALDERÓN’S INVERSE PROBLEM WITH A FINITE NUMBER OF MEASUREMENTS

• Authors : GIOVANNI S. ALBERTI; MATTEO SANTACESARIA

Subjects: 35R30 (primary); 42C40; 94A20 (secondary)

• Source: Forum of Mathematics, Sigma, Vol 7 (2019)

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The Calogero-Franc¸oise Integrable System: Algebraic Geometry, Higgs Fields, and the Inverse Problem

• Authors : Rayan, Steven; Stanley, Thomas; Szmigielski, Jacek

• Source: Integrable Systems and Algebraic Geometry ; page 356-382 ; ISBN 9781108773287 9781108715744

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Inverse problem for upper asymptotic density II

• Authors : Jin, Renling

• Source: Nonstandard Methods and Applications in Mathematics ; page 147-173

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Surfaces around closed principal curvature lines, an inverse problem

• Authors : Garcia, R.; Mello, L. F.; Sotomayor, J.

• Source: Real and Complex Singularities ; page 158-166 ; ISBN 9780521169691 9780511731983

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Statistical constraints on climate model parameters using a scalable cloud-based inference framework

• Authors : James Carzon; Bruno Abreu; Leighton Regayre

Subjects: gaussian process emulator; inverse problem; model discrepancy

• Source: Environmental Data Science, Vol 2 (2023)

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Reification of galaxies: cognitive astrophysics and the multiwavelength inverse problem.

• Authors : Madore, Barry F.¹

• Source: Proceedings of the International Astronomical Union. Aug2011 Supplement, Vol. 7 Issue S284, p1-1. 1p.

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Bayesian deconvolution of a rotating spectral line profile to a non-rotating one.

• Authors : Curé, M.¹ (AUTHOR) ; Escarate, P.² (AUTHOR); Celedon, L.^1,3 (AUTHOR)

• Source: Proceedings of the International Astronomical Union. Aug2023, Vol. 19 Issue S368, p107-109. 3p.

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Instability in deep learning – when algorithms cannot compute uncertainty quantifications for neural networks

• Authors : Luca Eva Gazdag; Vegard Antun; Anders C. Hansen

Subjects: Uncertainty quantification; non-computability; neural networks

• Source: European Journal of Applied Mathematics, Vol 37, Pp 290-312 (2026)

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