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Tag: bayesian

  • Singular Bayesian Neural Networks

    Singular Bayesian Neural Networks arXiv:2602.00387v1 Announce Type: new Abstract: Bayesian neural networks promise calibrated uncertainty but require $O(mn)$ parameters for standard mean-field Gaussian posteriors. We argue this cost is often unnecessary, particularly when weight matrices exhibit fast singular value decay. By parameterizing weights as $W = AB^{top}$ with $A in mathbb{R}^{m times r}$, $B in…

  • A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift

    A Statistical Assessment of Amortized Inference Under Signal-to-Noise Variation and Distribution Shift arXiv:2601.07944v1 Announce Type: new Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex and large-scale predictive problems. The recent success of deep neural networks and foundation models has…

  • A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference

    A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference arXiv:2601.05355v1 Announce Type: new Abstract: Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing conditional inference methods lack this flexibility as they are tied to a fixed conditioning structure and cannot perform…

  • Generative Bayesian Hyperparameter Tuning

    Generative Bayesian Hyperparameter Tuning arXiv:2512.20051v1 Announce Type: new Abstract: noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper-parameter learning can be difficult due to the cost of posterior sampling. We develop a generative…

  • BayesSum: Bayesian Quadrature in Discrete Spaces

    BayesSum: Bayesian Quadrature in Discrete Spaces arXiv:2512.16105v1 Announce Type: new Abstract: This paper addresses the challenging computational problem of estimating intractable expectations over discrete domains. Existing approaches, including Monte Carlo and Russian Roulette estimators, are consistent but often require a large number of samples to achieve accurate results. We propose a novel estimator, emph{BayesSum}, which…

  • Bayesian Optimization for Function-Valued Responses under Min-Max Criteria

    Bayesian Optimization for Function-Valued Responses under Min-Max Criteria arXiv:2512.07868v1 Announce Type: cross Abstract: Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settings the response is functional, varying smoothly over an index such as time or wavelength, which makes classical…

  • Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing

    Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing arXiv:2512.02495v1 Announce Type: new Abstract: Inverse problems arise across scientific and engineering domains, where the goal is to infer hidden parameters or physical fields from indirect and noisy observations. Classical approaches, such as variational regularization and Bayesian inference, provide well established theoretical…

  • Bayesian–AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence

    Bayesian–AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence arXiv:2511.11983v1 Announce Type: new Abstract: Modern epidemiological analytics increasingly use machine learning models that offer strong prediction but often lack calibrated uncertainty. Bayesian methods provide principled uncertainty quantification, yet are viewed as difficult to integrate with contemporary AI workflows. This paper proposes…

  • Bayesian Evaluation of Large Language Model Behavior

    Bayesian Evaluation of Large Language Model Behavior arXiv:2511.10661v1 Announce Type: cross Abstract: It is increasingly important to evaluate how text generation systems based on large language models (LLMs) behave, such as their tendency to produce harmful output or their sensitivity to adversarial inputs. Such evaluations often rely on a curated benchmark set of input prompts…

  • Robust Experimental Design via Generalised Bayesian Inference

    Robust Experimental Design via Generalised Bayesian Inference arXiv:2511.07671v1 Announce Type: new Abstract: Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain from selecting a certain design. However, accurate Bayesian inference relies on the assumption that one’s statistical model of…

  • Bayesian neural networks with interpretable priors from Mercer kernels

    Bayesian neural networks with interpretable priors from Mercer kernels arXiv:2510.23745v1 Announce Type: new Abstract: Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or noisy data. Bayesian neural networks (BNNs) provide a framework for this purpose by constructing…

  • Input Adaptive Bayesian Model Averaging

    Input Adaptive Bayesian Model Averaging arXiv:2510.22054v1 Announce Type: new Abstract: This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where different models may be better suited to different inputs. We propose input adaptive Bayesian Model Averaging (IA-BMA), a Bayesian method…

  • A Bayesian Framework for Symmetry Inference in Chaotic Attractors

    A Bayesian Framework for Symmetry Inference in Chaotic Attractors arXiv:2510.16509v1 Announce Type: new Abstract: Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical systems, symmetries encode structural properties of the dynamics that enable model reduction, principled comparison across conditions,…

  • Reliable data clustering with Bayesian community detection

    Reliable data clustering with Bayesian community detection arXiv:2510.15013v1 Announce Type: new Abstract: From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround…

  • A unified Bayesian framework for adversarial robustness

    A unified Bayesian framework for adversarial robustness arXiv:2510.09288v1 Announce Type: new Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical security challenge. Traditional defenses, such as adversarial training, typically robustify models by minimizing a worst-case loss. However, these deterministic approaches do not account for uncertainty in the adversary’s attack. While stochastic…

  • Bayesian Nonparametric Dynamical Clustering of Time Series

    Bayesian Nonparametric Dynamical Clustering of Time Series arXiv:2510.06919v1 Announce Type: new Abstract: We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the…

  • BaMANI: Bayesian Multi-Algorithm causal Network Inference

    BaMANI: Bayesian Multi-Algorithm causal Network Inference arXiv:2508.11741v1 Announce Type: new Abstract: Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also…

  • 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…

  • 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…

  • On Reconstructing Training Data From Bayesian Posteriors and Trained Models

    On Reconstructing Training Data From Bayesian Posteriors and Trained Models arXiv:2507.18372v1 Announce Type: new Abstract: Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reconstruction attacks, a major vulnerability of modern machine learning methods. This paper makes three…

  • Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes

    Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes arXiv:2507.12878v1 Announce Type: new Abstract: The identification of Linear Time-Variant (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system’s impulse response, $h(t, tau)$, as a stochastic…

  • LLMs are Bayesian, in Expectation, not in Realization

    LLMs are Bayesian, in Expectation, not in Realization arXiv:2507.11768v1 Announce Type: new Abstract: Large language models demonstrate remarkable in-context learning capabilities, adapting to new tasks without parameter updates. While this phenomenon has been successfully modeled as implicit Bayesian inference, recent empirical findings reveal a fundamental contradiction: transformers systematically violate the martingale property, a cornerstone requirement…

  • Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

    Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection arXiv:2507.11136v1 Announce Type: new Abstract: Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model…

  • The Bayesian Approach to Continual Learning: An Overview

    The Bayesian Approach to Continual Learning: An Overview arXiv:2507.08922v1 Announce Type: new Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps. Importantly, the learner is required to extend and update its knowledge without forgetting about the learning experience acquired from the past, and…

  • Bayesian Double Descent

    Bayesian Double Descent arXiv:2507.07338v1 Announce Type: new Abstract: Double descent is a phenomenon of over-parameterized statistical models. Our goal is to view double descent from a Bayesian perspective. Over-parameterized models such as deep neural networks have an interesting re-descending property in their risk characteristics. This is a recent phenomenon in machine learning and has been…

  • How’s the job market for Bayesian statistics?

    How’s the job market for Bayesian statistics? I’m a data scientist with 1 YOE. mostly worked on credit scoring models, sql, and Power BI. Lately, I’ve been thinking of going deeper into bayesian statistics and I’m currently going through the statistical rethinking book. But I’m wondering. is it worth focusing heavily on bayesian stats? Or…

  • A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python

    A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python Learn Causal Structures and make inferences with Bayesian Methods: Python Tutorial The post A Practical Starters’ Guide to Causal Structure Learning with Bayesian Methods in Python appeared first on Towards Data Science. Erdogan Taskesen Go to original source

  • Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking

    Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking arXiv:2506.09441v1 Announce Type: new Abstract: Tracking multiple particles in noisy and cluttered scenes remains challenging due to a combinatorial explosion of trajectory hypotheses, which scales super-exponentially with the number of particles and frames. The transformer architecture has shown a significant improvement in robustness against this high combinatorial…

  • 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC

    10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC Using GPU acceleration to speed up Bayesian Inference from months to minutes… The post 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC appeared first on Towards Data Science. Derek Tran Go to original source

  • Position: There Is No Free Bayesian Uncertainty Quantification

    Position: There Is No Free Bayesian Uncertainty Quantification arXiv:2506.03670v1 Announce Type: new Abstract: Due to their intuitive appeal, Bayesian methods of modeling and uncertainty quantification have become popular in modern machine and deep learning. When providing a prior distribution over the parameter space, it is straightforward to obtain a distribution over the parameters that is…

  • Bayesian Data Sketching for Varying Coefficient Regression Models

    Bayesian Data Sketching for Varying Coefficient Regression Models arXiv:2506.00270v1 Announce Type: new Abstract: Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large data applications, primarily due to prohibitively slow posterior computations using Markov chain Monte Carlo (MCMC) algorithms. We introduce Bayesian…

  • Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models

    Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models Explore how Bayesian Optimization outperforms Grid Search in efficiency and performance over binary classification tasks. The post Bayesian Optimization for Hyperparameter Tuning of Deep Learning Models appeared first on Towards Data Science. Kuriko Iwai Go to original source

  • Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles

    Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles arXiv:2505.13585v1 Announce Type: new Abstract: This work introduces a new method called scalable Bayesian Monte Carlo (SBMC). The model interpolates between a point estimator and the posterior, and the algorithm is a parallel implementation of a consistent (asymptotically unbiased) Bayesian deep learning algorithm: sequential Monte…

  • DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

    DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information arXiv:2505.05842v1 Announce Type: cross Abstract: Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients to participate in OFL, it is crucial to offer appropriate incentives to offset…

  • Bayesian learning of the optimal action-value function in a Markov decision process

    Bayesian learning of the optimal action-value function in a Markov decision process arXiv:2505.01859v1 Announce Type: new Abstract: The Markov Decision Process (MDP) is a popular framework for sequential decision-making problems, and uncertainty quantification is an essential component of it to learn optimal decision-making strategies. In particular, a Bayesian framework is used to maintain beliefs about…

  • Are You Sure Your Posterior Makes Sense?

    Are You Sure Your Posterior Makes Sense? This article is co-authored by Felipe Bandeira, Giselle Fretta, Thu Than, and Elbion Redenica. We also thank Prof. Carl Scheffler for his support. Introduction Parameter estimation has been for decades one of the most important topics in statistics. While frequentist approaches, such as Maximum Likelihood Estimations, used to…

  • Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows

    Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows arXiv:2504.04013v1 Announce Type: new Abstract: Post-earthquake hazard and impact estimation are critical for effective disaster response, yet current approaches face significant limitations. Traditional models employ fixed parameters regardless of geographical context, misrepresenting how seismic effects vary across diverse…

  • Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

    Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks arXiv:2503.10496v1 Announce Type: new Abstract: Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from over-parameterization, complicating interpretation and raising uncertainty issues. Bayesian neural networks (BNNs) address the latter by representing weights as probability distributions, allowing…

  • A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection

    A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection arXiv:2502.08695v1 Announce Type: new Abstract: Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we…

  • Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data

    Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data arXiv:2501.13483v1 Announce Type: new Abstract: Neural amortized Bayesian inference (ABI) can solve probabilistic inverse problems orders of magnitude faster than classical methods. However, neural ABI is not yet sufficiently robust for widespread and safe applicability. In particular, when performing inference on observations outside of the…

  • Covariate Dependent Mixture of Bayesian Networks

    Covariate Dependent Mixture of Bayesian Networks arXiv:2501.05745v1 Announce Type: new Abstract: Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a…

  • Bayesian A/B Testing Falls Short

    Bayesian A/B Testing Falls Short Why Bayesian A/B testing can lead to misunderstandings, inflated false positive rates, introduce bias and complicate results (Image generated by the author using Midjourney) Over the past decade, I’ve engaged in countless discussions about Bayesian A/B testing versus Frequentist A/B testing. In nearly every conversation, I’ve maintained the same viewpoint:…

  • Adaptive Nonparametric Perturbations of Parametric Bayesian Models

    Adaptive Nonparametric Perturbations of Parametric Bayesian Models arXiv:2412.10683v2 Announce Type: cross Abstract: Parametric Bayesian modeling offers a powerful and flexible toolbox for scientific data analysis. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we study nonparametrically perturbed parametric (NPP) Bayesian models, in which a parametric…

  • Semiparametric Bayesian Difference-in-Differences

    Semiparametric Bayesian Difference-in-Differences arXiv:2412.04605v1 Announce Type: cross Abstract: This paper studies semiparametric Bayesian inference for the average treatment effect on the treated (ATT) within the difference-in-differences research design. We propose two new Bayesian methods with frequentist validity. The first one places a standard Gaussian process prior on the conditional mean function of the control group.…