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Paper Citation Record · LEDGER

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.22330.

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pith.paper-citation-record.v1
2607.22330 v1

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measured 27 of 27 reference resolution

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Outbound references

Observation 0a983977-0ea5-44ea-8b42-3701d7d7dff5 · outbound

This paper cites Dust spectral energy distribution in the era of Herschel and Planck: A hierarchical Bayesian-futting technique,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Dust spectral energy distribution in the era of Herschel and Planck: A hierarchical Bayesian-futting technique,

Reference 1

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This paper cites Beetroots: Spatially regularized bayesian inference of physical parameter maps. application to orion,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Beetroots: Spatially regularized bayesian inference of physical parameter maps. application to orion,

Reference 2

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This paper cites Oriented speckle reducing anisotropic diffusion,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Oriented speckle reducing anisotropic diffusion,

Reference 3

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Observation bb9c6aa1-40d3-47c0-a92f-63b2c8a0193f · outbound

This paper cites Multiplicative noise removal using l1 fidelity on frame coefficients,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Multiplicative noise removal using l1 fidelity on frame coefficients,

Reference 4

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This paper cites A nonlinear inverse scale space method for a convex multiplicative noise model,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise A nonlinear inverse scale space method for a convex multiplicative noise model,

Reference 5

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This paper cites An additive approximation to multiplica- tive noise,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise An additive approximation to multiplica- tive noise,

Reference 6

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This paper cites The convex relaxation method on deconvolution model with multiplicative noise,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise The convex relaxation method on deconvolution model with multiplicative noise,

Reference 7

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This paper cites Efficient sampling of non log-concave posterior distribu- tions with mixture of noises,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Efficient sampling of non log-concave posterior distribu- tions with mixture of noises,

Reference 8

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This paper cites An algorithm for solving the inverse problem in total internal reflection microscopy,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise An algorithm for solving the inverse problem in total internal reflection microscopy,

Reference 9

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This paper cites Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning

Reference 10

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Unresolved cited work

Reference 11

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This paper cites An adaptive surrogate modeling based on deep neural networks for large-scale bayesian inverse problems,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise An adaptive surrogate modeling based on deep neural networks for large-scale bayesian inverse problems,

Reference 12

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This paper cites Methods for stochastic col- lection and replenishment (scar) optimisation for persistent autonomy,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Methods for stochastic col- lection and replenishment (scar) optimisation for persistent autonomy,

Reference 13

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Inverse problems: From regularization to bayesian inference,

Reference 14

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Convergence of condi- tional metropolis-hastings samplers,

Reference 15

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This paper cites Parallel gibbs sampling: From colored fields to thin junction trees,.

A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Parallel gibbs sampling: From colored fields to thin junction trees,

Reference 16

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise The multiple-try method and local optimization in metropolis sampling,

Reference 18

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Langevin diffusions and the metropolis-adjusted langevin algorithm,

Reference 19

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Exponential convergence of langevin distributions and their discrete approximations,

Reference 20

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise A review of multiple try mcmc algorithms for signal processing,

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Unresolved cited work

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise A model for atomic and molecular interstellar gas: The meudon pdr code,

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Neural network-based emulation of interstellar medium models,

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Bayes factors,

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Understanding predictive infor- mation criteria for bayesian models,

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A Hierarchical Likelihood Model for Non-linear Inverse Problems under Additive and Multiplicative Noise Nocedal and S

Reference 27

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