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аналитика - Bayesian Inverse Problems
Denoising Diffusion Priors: Divide-and-Conquer Posterior Sampling
Efficiently sample from posterior using DCPS for denoising diffusion priors.
Multilevel Markov Chain Monte Carlo for Bayesian Inverse Problems in Navier-Stokes Equation with Lagrangian Observations
Efficient Multilevel MCMC method for solving Bayesian inverse problems in Navier-Stokes equations with Lagrangian observations.
A Data-Adaptive Prior Based on Reproducing Kernel Hilbert Space for Bayesian Learning of Kernels in Operators
This paper proposes a data-adaptive prior based on Reproducing Kernel Hilbert Space (RKHS) for Bayesian learning of kernels in operators, addressing the instability of posterior mean in traditional Bayesian methods with non-degenerate priors, particularly in the small noise regime.
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