A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems | Paper Citation Record

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This paper cites The method of the approximate inverse for atmo- spheric tomography.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems The method of the approximate inverse for atmo- spheric tomography

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Optical tomography in medical imaging

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Risks for the

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Double-loop randomized quasi-Monte Carlo estimator for nested integration

Reference 4

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This paper cites Upper and lower bounds for local Lipschitz stability of Bayesian posteriors.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Upper and lower bounds for local Lipschitz stability of Bayesian posteriors

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This paper cites Truncated proposals for scalable and hassle-free simulation-based inference.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Truncated proposals for scalable and hassle-free simulation-based inference

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This paper cites Deep Surrogate Accelerated Delayed- Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Deep Surrogate Accelerated Delayed- Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems

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This paper cites The Statistical Accuracy of Neural Posterior and Likelihood Estimation.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems The Statistical Accuracy of Neural Posterior and Likelihood Estimation

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This paper cites Bayesian Optimization for Likelihood-Free In- ference of Simulator-Based Statistical Models.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bayesian Optimization for Likelihood-Free In- ference of Simulator-Based Statistical Models

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bayesian optimal experimental design with Wasserstein information criteria

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This paper cites In preparation.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems In preparation

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This paper cites Introduction to Gaussian Process Regression in Bayesian Inverse Prob- lems, with New Results on Experimental Design for Weighted Error Measures.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Introduction to Gaussian Process Regression in Bayesian Inverse Prob- lems, with New Results on Experimental Design for Weighted Error Measures

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This paper cites Deep neural network expression of posterior expectations in Bayesian PDE inversion.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Deep neural network expression of posterior expectations in Bayesian PDE inversion

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This paper cites Hikida, A.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Hikida, A

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Electrical Impedance Tomography: Methods, History and Applications

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This paper cites Kaipio and E.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Kaipio and E

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This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

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This paper cites Discretization-invariant Bayesian inver- sion and Besov space priors.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Discretization-invariant Bayesian inver- sion and Besov space priors

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This paper cites Linear inverse problems for gen- eralised random variables.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Linear inverse problems for gen- eralised random variables

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This paper cites Surrogate modeling for Bayesian inverse prob- lems based on physics-informed neural networks.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Surrogate modeling for Bayesian inverse prob- lems based on physics-informed neural networks

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This paper cites Random Forward Models and Log- Likelihoods in Bayesian Inverse Problems.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Random Forward Models and Log- Likelihoods in Bayesian Inverse Problems

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Likelihood-free inference with emulator networks

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bayesian synthetic likelihood for stochastic models with applications in mathematical finance

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Approximate Bayesian computational methods

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This paper cites A Stochastic Collocation Approach to Bayesian Infer- ence in Inverse Problems.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems A Stochastic Collocation Approach to Bayesian Infer- ence in Inverse Problems

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems GPS-ABC: Gaussian process surrogate approximate Bayesian computation

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Unresolved cited work

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bracketing Metric Entropy Rates and Empirical Central Limit Theorems for Function Classes of Besov- and Sobolev-Type

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Estimating the Transmission Dynamics of Streptococcus pneumo- niae from Strain Prevalence Data

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Neural Density Estimation and Likelihood-free Inference

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Fast epsilon-free Inference of Simulation Models with Bayesian Conditional Density Estimation

Reference 32

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Masked Autoregressive Flow for Density Estimation

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems BayesFlow: Learning complex stochastic models with invertible neural networks

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems On Nesting Monte Carlo Estimators

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This paper cites Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

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This paper cites Thomas Hofmann, Bernhard Sch¨ olkopf, and Alexander J Smola.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Thomas Hofmann, Bernhard Sch¨ olkopf, and Alexander J Smola

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This paper cites Lec- ture notes.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Lec- ture notes

Reference 40

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Sequential Monte Carlo without likelihoods

Reference 41

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This paper cites On the local Lipschitz stability of Bayesian inverse problems.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems On the local Lipschitz stability of Bayesian inverse problems

Reference 42

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This paper cites Acta Numerica 19, 451– 559 (2010) https://doi.org/10.1017/S0962492910000061.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Acta Numerica 19, 451– 559 (2010) https://doi.org/10.1017/S0962492910000061

Reference 43

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This paper cites Posterior consistency for Gaussian process approximations of Bayesian posterior distributions.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Posterior consistency for Gaussian process approximations of Bayesian posterior distributions

Reference 44

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This paper cites Bayesian Inversion for the Identification of the Doping Profile in Unipolar Semiconductor Devices.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bayesian Inversion for the Identification of the Doping Profile in Unipolar Semiconductor Devices

Reference 45

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This paper cites van der Vaart and J.A.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems van der Vaart and J.A

Reference 46

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A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Unresolved cited work

Reference 47

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This paper cites Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods

Reference 48

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This paper cites Sequential Bayesian Design for Efficient Surrogate Construction in the Inversion of Darcy Flows.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Sequential Bayesian Design for Efficient Surrogate Construction in the Inversion of Darcy Flows

Reference 49

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This paper cites Statistical inference for noisy nonlinear ecological dynamic systems.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Statistical inference for noisy nonlinear ecological dynamic systems

Reference 50

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This paper cites Princeton University Press, 2010.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Princeton University Press, 2010

Reference 51

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Observation a0974e8e-464b-447b-bc42-3823baa95a6e · outbound

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

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T12:14:51.645125Z

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Observation c749a0bd-cff7-4802-9018-cce281c43f0c · outbound

This paper cites Masset, R.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Masset, R

Reference 53

Resolution: malformed identifier; doi_truncated, observed 2026-07-08T12:14:51.312473Z

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