The following pages link to Inverse problems as statistics (Q3146252):
Displaying 40 items.
- Approximate maximum entropy on the mean for instrumental variable regression (Q433589) (← links)
- Comparing parameter choice methods for regularization of ill-posed problems (Q551477) (← links)
- A statistical perspective on ill-posed inverse problems (with discussion) (Q579825) (← links)
- Stochastic spectral methods for efficient Bayesian solution of inverse problems (Q886042) (← links)
- Efficient calibration for imperfect computer models (Q892237) (← links)
- Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems (Q1009939) (← links)
- Statistical methods for inverse problems. Abstracts from the mini-workshop held November 26 -- December 2, 2006. (Q1046963) (← links)
- A unified approach to inversion problems in statistics (Q1332110) (← links)
- Observable dictionary learning for high-dimensional statistical inference (Q1639590) (← links)
- Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018 (Q1731979) (← links)
- Statistical and computational inverse problems. (Q1763141) (← links)
- Periodic boxcar deconvolution and Diophantine approximation (Q1766117) (← links)
- Estimating anomalies from indirect observations (Q1851278) (← links)
- Statistical methodology for inverse problems (Q1905840) (← links)
- Where Bayes tweaks Gauss: conditionally Gaussian priors for stable multi-dipole estimation (Q1983459) (← links)
- A new look at Akaike's Bayesian information criterion for inverse ill-posed problems (Q2027449) (← links)
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems (Q2214560) (← links)
- Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach (Q2291491) (← links)
- Joint inversion of compact operators (Q2301658) (← links)
- On the lifting of deterministic convergence rates for inverse problems with stochastic noise (Q2360781) (← links)
- Maximum entropy solution to ill-posed inverse problems with approximately known operator (Q2427763) (← links)
- Statistical inverse problems: discretization, model reduction and inverse crimes (Q2508956) (← links)
- Fisher information for inverse problems and trace class operators (Q2872413) (← links)
- Regularized posteriors in linear ill-posed inverse problems (Q2911714) (← links)
- Constraints versus Priors (Q2945168) (← links)
- A new network approach to Bayesian inference in partial differential equations (Q2952823) (← links)
- Bayesian analysis in inverse problems (Q3985014) (← links)
- Investigation of regularization parameters and error estimating in inverse elasticity problems (Q4299519) (← links)
- Random Forward Models and Log-Likelihoods in Bayesian Inverse Problems (Q4611529) (← links)
- Task adapted reconstruction for inverse problems (Q5081802) (← links)
- Objective Frequentist Uncertainty Quantification for Atmospheric \(\mathrm{CO}_2\) Retrievals (Q5097848) (← links)
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems (Q5162376) (← links)
- Solving inverse problems using data-driven models (Q5230520) (← links)
- Stochastic Collocation Algorithms Using $l_1$-Minimization for Bayesian Solution of Inverse Problems (Q5254808) (← links)
- A Study on Regularization for Discrete Inverse Problems with Model-Dependent Noise (Q5359494) (← links)
- Predictive risk estimation for the expectation maximization algorithm with Poisson data (Q5859746) (← links)
- Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems (Q6194148) (← links)
- A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion (Q6553787) (← links)
- A new bi-fidelity model reduction method for Bayesian inverse problems (Q6553850) (← links)
- Learning nonlocal weights for second-order nonlocal super-resolution (Q6657917) (← links)
