Tag: prior
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Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
Estimation of instrument and noise parameters for inverse problem based on prior diffusion model arXiv:2602.11711v1 Announce Type: new Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a…
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Error Analysis of Bayesian Inverse Problems with Generative Priors
Error Analysis of Bayesian Inverse Problems with Generative Priors arXiv:2601.17374v1 Announce Type: new Abstract: Data-driven methods for the solution of inverse problems have become widely popular in recent years thanks to the rise of machine learning techniques. A popular approach concerns the training of a generative model on additional data to learn a bespoke prior…
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Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models
Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models arXiv:2512.21593v1 Announce Type: new Abstract: Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple prior, typically a standard normal distribution, into the target…
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Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry arXiv:2509.02617v1 Announce Type: new Abstract: Parametric partial differential equations (PDEs) are fundamental mathematical tools for modeling complex physical systems, yet their numerical evaluation across parameter spaces remains computationally intensive when using conventional high-fidelity solvers. To address this challenge, we propose a…
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Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge arXiv:2508.15345v1 Announce Type: new Abstract: Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge and assumptions about the system. However, the fusion of…
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Formal Bayesian Transfer Learning via the Total Risk Prior
Formal Bayesian Transfer Learning via the Total Risk Prior arXiv:2507.23768v1 Announce Type: new Abstract: In analyses with severe data-limitations, augmenting the target dataset with information from ancillary datasets in the application domain, called source datasets, can lead to significantly improved statistical procedures. However, existing methods for this transfer learning struggle to deal with situations where…
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Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior arXiv:2504.18455v1 Announce Type: new Abstract: We study the problem of distributed multi-view representation learning. In this problem, $K$ agents observe each one distinct, possibly statistically correlated, view and independently extracts from it a suitable representation in a manner that a…
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A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior
A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior arXiv:2504.06372v1 Announce Type: cross Abstract: Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form…