Cover Image

Beyond Independence: on Jointly Normal Priors in Bayesian Inversion

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nicholson, Ruanui, Niskanen, Matti, Maclaren, Oliver J., Kaipio, Jari P.
Format:	Preprint
Published:	2026
Subjects:	Methodology Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Non-intrusive optimal experimental design for large-scale nonlinear Bayesian inverse problems using a Bayesian approximation error approach
by: Koval, Karina, et al.
Published: (2024)

Network inference via approximate Bayesian computation. Illustration on a stochastic multi-population neural mass model
by: Ditlevsen, Susanne, et al.
Published: (2023)

Bayesian optimal experimental design with Wasserstein information criteria
by: Helin, Tapio, et al.
Published: (2025)

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty
by: Alexanderian, Alen, et al.
Published: (2022)

A stopping rule for randomly sampling bipartite networks with fixed degree sequences
by: Neal, Zachary P.
Published: (2023)

Sequential multiple importance sampling for high-dimensional Bayesian inference
by: Binbin, Li, et al.
Published: (2025)

Fast nonparametric spectral density estimation from irregularly sampled data
by: Geoga, Christopher J., et al.
Published: (2025)

Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
by: Miyatake, Yuto, et al.
Published: (2024)

ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
by: Furset, S. Knutsen, et al.
Published: (2026)

Quantile Randomized Kaczmarz Algorithm with Whitelist Trust Mechanism
by: Shvaiko, Sofiia, et al.
Published: (2026)

Efficient and Precise Calculation of the Confluent Hypergeometric Function
by: Herschtal, Alan
Published: (2024)

Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications
by: Su, Yan-Wei, et al.
Published: (2023)

Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions
by: Iguchi, Yuga, et al.
Published: (2022)

Sparse optimization for estimating the cross-power spectrum in linear inverse models : from theory to the application in brain connectivity
by: Carini, Laura, et al.
Published: (2024)

On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
by: Weissmann, Simon, et al.
Published: (2024)

The Ensemble Kalman Filter for Dynamic Inverse Problems
by: Weissmann, Simon, et al.
Published: (2024)

Cubature-based uncertainty estimation for nonlinear regression models
by: Bubel, Martin, et al.
Published: (2024)

Unbiased Parameter Estimation of Partially Observed Diffusions using Diffusion Bridges
by: Alvarez, Miguel, et al.
Published: (2025)

Numerically robust square root implementations of statistical linear regression filters and smoothers
by: Tronarp, Filip
Published: (2024)

Efficient spline orthogonal basis for representation of density functions
by: Burkotová, Jana, et al.
Published: (2024)

Analysis of Geometric Selection of the Data-Error Covariance Inflation for ES-MDA
by: Emerick, Alexandre A.
Published: (2018)

Parameter Estimation for Partially Observed McKean-Vlasov Diffusions
by: Jasra, Ajay, et al.
Published: (2024)

Fast Machine-Precision Spectral Likelihoods for Stationary Time Series
by: Geoga, Christopher J.
Published: (2024)

Fast and accurate conditioning for large-scale and online Gaussian process prediction problems
by: Arora, Samanyu, et al.
Published: (2026)

Stein transport for Bayesian inference
by: Nüsken, Nikolas
Published: (2024)

Probabilistic Richardson Extrapolation
by: Oates, Chris. J., et al.
Published: (2024)

Parameter Estimation for Partially Observed Time-Changed SDEs
by: Zhao, Ke, et al.
Published: (2026)

Optimal experimental design: Formulations and computations
by: Huan, Xun, et al.
Published: (2024)

Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications
by: Iguchi, Yuga, et al.
Published: (2024)

Improving sampling efficacy on high dimensional distributions with thin high density regions using Conservative Hamiltonian Monte Carlo
by: McGregor, Geoffrey, et al.
Published: (2022)

Adaptive multi-stage integration schemes for Hamiltonian Monte Carlo
by: Nagar, Lorenzo, et al.
Published: (2023)

Roughness regularization for functional data analysis with free knots spline estimation
by: De Magistris, Anna, et al.
Published: (2024)

Variational Markov chain mixtures with automatic component selection
by: Miles, Christopher E., et al.
Published: (2024)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
by: Paulin, Daniel, et al.
Published: (2024)

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
by: Gong, Xindi, et al.
Published: (2026)

Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
by: Nicholson, Ruanui, et al.
Published: (2025)

Deep Neural-network Prior for Orbit Recovery from Method of Moments
by: Khoo, Yuehaw, et al.
Published: (2023)

Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
by: Tong, Xin T., et al.
Published: (2026)

Kinetic Langevin Splitting Schemes for Constrained Sampling
by: Chada, Neil K., et al.
Published: (2026)

Denoising deterministic networks using iterative Fourier transforms
by: Frost, H. Robert
Published: (2026)