mailitics

Category: physics.data-an

  • Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

    Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference arXiv:2510.21889v1 Announce Type: new Abstract: Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimilative causal inference (ACI), integrates observations with dynamical models. It utilizes Bayesian data assimilation…

  • 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…

  • Cryo-EM as a Stochastic Inverse Problem

    Cryo-EM as a Stochastic Inverse Problem arXiv:2509.05541v1 Announce Type: new Abstract: Cryo-electron microscopy (Cryo-EM) enables high-resolution imaging of biomolecules, but structural heterogeneity remains a major challenge in 3D reconstruction. Traditional methods assume a discrete set of conformations, limiting their ability to recover continuous structural variability. In this work, we formulate cryo-EM reconstruction as a stochastic…

  • Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective

    Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective arXiv:2507.19540v1 Announce Type: new Abstract: Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we…

  • Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators

    Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators arXiv:2507.14652v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s…

  • On the emergence of numerical instabilities in Next Generation Reservoir Computing

    On the emergence of numerical instabilities in Next Generation Reservoir Computing arXiv:2505.00846v1 Announce Type: new Abstract: Next Generation Reservoir Computing (NGRC) is a low-cost machine learning method for forecasting chaotic time series from data. However, ensuring the dynamical stability of NGRC models during autonomous prediction remains a challenge. In this work, we uncover a key…

  • Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines

    Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines arXiv:2504.05643v1 Announce Type: new Abstract: Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem with a complete dataset requires computing both data and model expectations and…

  • 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…

  • Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve

    Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve arXiv:2501.08288v1 Announce Type: new Abstract: Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x = ab$. For…

  • An information theoretic limit to data amplification

    An information theoretic limit to data amplification arXiv:2412.18041v1 Announce Type: new Abstract: In recent years generative artificial intelligence has been used to create data to support science analysis. For example, Generative Adversarial Networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the…

  • GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction

    GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction arXiv:2412.08661v1 Announce Type: new Abstract: Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally…

  • MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle

    MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle arXiv:2412.02090v1 Announce Type: new Abstract: Maximum entropy principle (MEP) offers an effective and unbiased approach to inferring unknown probability distributions when faced with incomplete information, while neural networks provide the flexibility to learn complex distributions from data. This paper proposes a novel…