Probability & Statistics · Mathematics & Statistics
Methodology
The newest papers in Methodology that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
353 papers, from 4 September 2026 to 5 November 2026: 353 arXiv preprints.
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Newest papers, page 8
Copula Active Subspaces II: Error Decomposition, A Posteriori Estimation, and Sharpness of the Bounds
The Copula Active Subspaces (CAS) method of Part I estimates a non-Gaussian observation-noise density $π_{\boldsymbol{n}}$ from residual samples.An Algebraic Approach to Constrained K-means Clustering
Clustering is typically performed using only object features, although additional information about relationships between objects may be available and can be incorporated through constraints.Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds
Data and the governing partial differential equations (PDEs) of continuous-time dynamical systems provide rich structure for statistical learning.Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes
Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows.From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance
Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differences involving the smaller sample.Spatial Prediction with Guided Diffusion
Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data.Kernel-based External Validity of Experimental Studies
The external validity of a study is the extent to which its findings generalize to unseen experimental conditions, such as datasets.Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields
Latent field models are essential for spatial statistics, but the posterior distribution of the latent field, even with a Gaussian process (GP) prior, is analytically intractable whenever the…SLICE: Sequential Likelihood-Free Inference via Conformal E-Values
Many scientific models can be simulated but have intractable likelihoods, making frequentist uncertainty quantification difficult.De-biased Distribution Estimation for Locally Differential Private Survival Data
Sharing time-to-event data can leak sensitive information about individual participants in clinical studies.Regularized Small Area Estimation with Graph Laplacian Benchmarking priors
Small area estimation (SAE) often requires both borrowing information across areas and benchmarking estimates to reliable aggregates.How to post-train on a surrogate: Envelope sampling mitigates reward hacking
Large language models (LLMs) are commonly post-trained against LLM judges and other cheap surrogates because the true reward, such as human preference, is too expensive to query at scale.Open-ended Scientific Discovery with Possibilistic Reasoning
Autonomous scientific discovery with LLMs requires generating and testing hypotheses adaptively as evidence accumulates while maintaining statistical validity.Sample-Efficient Generative Conformal Prediction
Generative conformal prediction builds uncertainty sets from samples of a conditional generator, which are efficient only when the samples represent the response distribution well.Estimating Great Expectations Under Autoregressive Language Models with Potentials
Many applications of language models hinge not on individual samples but on the expectation of a test functional under the model.Feature Space Adaptation for Effortless Gaussian Process Flows
Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging.Prediction-Powered Data Fusion for Treatment Effect Estimation
Randomized controlled trials (RCTs) identify treatment effects without confounding but are often small, whereas observational studies (OBS) are large but may be confounded.Marformer: A Transformer for Predicting Missing Data Distributions
Real decisions are made under incomplete information.Product Monte Carlo: Expectation Under a Product of Densities
Aggregating independently computed sub-sample inferences is a well-known challenge in large-scale Bayesian computation, federated learning, and opinion pooling.Bayesian Causal Forests for Zero-Inflated Continuous Outcomes
Estimating (heterogeneous) treatment effects is still challenging when the outcome is semicontinuous (i.e., exhibiting both a discrete point mass at zero and a skewed continuous distribution for…