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Tag: posterior

  • Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation

    Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation arXiv:2601.22367v1 Announce Type: new Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $beta>0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset…

  • Provable Diffusion Posterior Sampling for Bayesian Inversion

    Provable Diffusion Posterior Sampling for Bayesian Inversion arXiv:2512.08022v1 Announce Type: new Abstract: This paper proposes a novel diffusion-based posterior sampling method within a plug-and-play (PnP) framework. Our approach constructs a probability transport from an easy-to-sample terminal distribution to the target posterior, using a warm-start strategy to initialize the particles. To approximate the posterior score, we…

  • Robust variational neural posterior estimation for simulation-based inference

    Robust variational neural posterior estimation for simulation-based inference arXiv:2509.05724v1 Announce Type: new Abstract: Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochastic models. Although these methods have demonstrated reliable posterior estimation when the simulator accurately represents the underlying data generative process (GDP),…

  • Simulating Posterior Bayesian Neural Networks with Dependent Weights

    Simulating Posterior Bayesian Neural Networks with Dependent Weights arXiv:2507.22095v1 Announce Type: new Abstract: In this paper we consider posterior Bayesian fully connected and feedforward deep neural networks with dependent weights. Particularly, if the likelihood is Gaussian, we identify the distribution of the wide width limit and provide an algorithm to sample from the network. In…

  • From Global to Local: A Scalable Benchmark for Local Posterior Sampling

    From Global to Local: A Scalable Benchmark for Local Posterior Sampling arXiv:2507.21449v1 Announce Type: new Abstract: Degeneracy is an inherent feature of the loss landscape of neural networks, but it is not well understood how stochastic gradient MCMC (SGMCMC) algorithms interact with this degeneracy. In particular, current global convergence guarantees for common SGMCMC algorithms rely…

  • The surprising strength of weak classifiers for validating neural posterior estimates

    The surprising strength of weak classifiers for validating neural posterior estimates arXiv:2507.17026v1 Announce Type: new Abstract: Neural Posterior Estimation (NPE) has emerged as a powerful approach for amortized Bayesian inference when the true posterior $p(theta mid y)$ is intractable or difficult to sample. But evaluating the accuracy of neural posterior estimates remains challenging, with existing…

  • CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates

    CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates arXiv:2507.17030v1 Announce Type: new Abstract: We consider the problem of validating whether a neural posterior estimate ( q(theta mid x) ) is an accurate approximation to the true, unknown true posterior ( p(theta mid x) ). Existing methods for evaluating the quality…

  • Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality

    Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality arXiv:2506.19144v1 Announce Type: new Abstract: This work establishes that sparse Bayesian neural networks achieve optimal posterior contraction rates over anisotropic Besov spaces and their hierarchical compositions. These structures reflect the intrinsic dimensionality of the underlying function, thereby mitigating the curse of dimensionality. Our…

  • Robust and Scalable Variational Bayes

    Robust and Scalable Variational Bayes arXiv:2504.12528v1 Announce Type: new Abstract: We propose a robust and scalable framework for variational Bayes (VB) that effectively handles outliers and contamination of arbitrary nature in large datasets. Our approach divides the dataset into disjoint subsets, computes the posterior for each subset, and applies VB approximation independently to these posteriors.…

  • Uncertainty quantification and posterior sampling for network reconstruction

    Uncertainty quantification and posterior sampling for network reconstruction arXiv:2503.07736v1 Announce Type: new Abstract: Network reconstruction is the task of inferring the unseen interactions between elements of a system, based only on their behavior or dynamics. This inverse problem is in general ill-posed, and admits many solutions for the same observation. Nevertheless, the vast majority of…

  • Near-Optimal Approximations for Bayesian Inference in Function Space

    Near-Optimal Approximations for Bayesian Inference in Function Space arXiv:2502.18279v1 Announce Type: new Abstract: We propose a scalable inference algorithm for Bayes posteriors defined on a reproducing kernel Hilbert space (RKHS). Given a likelihood function and a Gaussian random element representing the prior, the corresponding Bayes posterior measure $Pi_{text{B}}$ can be obtained as the stationary distribution…