Probability & Statistics · Mathematics & Statistics
Computation
The newest papers in Computation that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
95 papers, from 4 September 2026 to 3 November 2026: 95 arXiv preprints.
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Newest papers, page 3
Spatial Prediction with Guided Diffusion
Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data.Enhancing credit risk scorecards with transactional-based behavioural features
Credit risk modelling is fundamental in financial institutions, typically using credit history, repayment behaviour, income, and outstanding debt to estimate a borrower's probability of default.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…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.Efficient quadratic entropy with distance sketches
We detail scalable methods for approximating the quadratic entropy $p^T d p$ for arbitrary distributions $p$ and common distances $d$ of negative type.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.Scalable Regularized Vector Multiplicative Error Models for Positive-valued Financial Time Series
The logarithmic multiplicative error model (log-vMEM) has been useful in modeling and forecasting multivariate positive-valued financial time series.Empirical-Bayes spectral partial pooling across related tasks
Spectral methods are central to high-dimensional statistics and machine learning, underlying procedures for covariance estimation, matrix denoising, representation learning, clustering, and latent…A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company
Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing.Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking
We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model.Efficient Mass Matrix Estimation with Gaussian Cooling
We introduce an algorithm for efficiently preconditioning log-concave and log-smooth distributions that scales logarithmically with the condition number of the underlying distribution.Low-Rank and Structured Sparse Tensor Decomposition for Anomaly Detection in Multivariate Functional Data
Multivariate functional data arise in many modern manufacturing systems, where multiple sensors record densely sampled process trajectories.A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data
With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains.On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal
Skew-symmetric probability distributions provide a principled mechanism for incorporating gradient information into Markov chain Monte Carlo algorithms.MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees
Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be distributed across correlated…Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems
Sampling from the posterior is the central task of computational Bayesian inverse problems.Learn-Then-Differentiate Gradient Estimation
Learn-then-differentiate (LTD) estimates gradients by fitting a model to simulation outputs and differentiating it.Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and offers near…Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods
We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation.Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure.