Probability & Statistics · Mathematics & Statistics
Applications
The newest papers in Applications that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
143 papers, from 4 September 2026 to 5 November 2026: 143 arXiv preprints.
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Newest papers, page 7
Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election
Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before elections.Predictive Likelihood Ratios for Language Model Watermark Detection
Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key.Multisource Remote Sensing and Geospatial Analysis of Vineyard Wildfire Impacts and Resilience: The 2019 Kincade Fire
Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems.Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries
Tracking the energy transition requires reliable statistics on renewable deployment.Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction
Fault detection is essential in industrial systems, enabling early identification of abnormal behaviour and improving safety, reliability, and operational efficiency.An Uncertainty-Aware Hybrid Mathematical-Machine-Learning Model for Smart Irrigation Decision Support
Agriculture accounts for roughly 70% of global freshwater withdrawals, yet irrigation is still commonly scheduled reactively, with no forecast of where soil moisture is heading and no statement of…Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data
Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across temporal and spatiotemporal forecasting…Buyer Artificial Intelligence-Enabled Environmental Governance and Supplier Environmental Controversies: An Organizational Information Processing and Signaling
Environmental controversies in global supply chains pose significant risks for global buyers.Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes
Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme…Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices
Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health.Geometric organization of olfactory descriptor data in the Poincaré disk
Odor quality is commonly represented using high dimensional descriptor profiles, yet their low dimensional organization remains unclear.Instability in Patient Clustering: A Multiverse Analysis of Unsupervised Clustering in the CENTER-TBI cohort
Understanding patient heterogeneity is key to improving prognostic modeling in traumatic brain injury (TBI).The Rater Ising-Potts Model with LLM-Derived Weights: An Application to Multi-Category Scoring Reliability
The Ising model is extended to the Potts model for multinomial data.Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity.Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples…Distributed Lag Neural Additive Models
We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags.Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts
Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging.Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation
We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term.Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure.When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability
LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide little evidence about whether they can reduce…