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 2
Generalizing Metropolis-Hastings for DAG Sampling
A fundamental challenge in Bayesian network structure learning lies in efficiently approximating the posterior distribution of directed acyclic graphs (DAGs).Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty.Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computationally prohibitive…Practical Advantages of Online Conformal Prediction for Data Streams
Full conformal prediction offers finite-sample marginal coverage guarantees under exchangeability, but exact computation can be costly, while its classical guarantees do not directly extend to…Federated Community Detection in Bipartite Networks
We study federated spectral estimation when a large matrix is horizontally partitioned across servers that cannot share raw data.lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
lepidoptera is an open-source R and Python package for simulation from structural causal models with heterogeneous variable types.Censored Flow Matching for Synthetic Time-to-Event Generation
Generating time-to-event data is increasingly important in survival analysis, but it remains statistically challenging because event times are often censored and thus prevent flexible generative…MCMC Can See Can't See
It is folklore that passing "convergence" diagnostics does not guarantee that a Markov chain has converged.Mixed Poisson diffusion models and application to posterior sampling
Diffusion models are a powerful framework for learning complex probability distributions.Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES
Biological age (BA) captures inter-individual variation in physiological aging beyond chronological age (CA).A Randomized Approximation Algorithm for Doubly Free-Boundary Macdonald Processes
We construct an approximate sampler for finite doubly free-boundary Macdonald rail-yard processes with Macdonald, boundary, and edge parameters in $(0,1)$.Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Reliable uncertainty quantification remains an important challenge in AI.Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output.Locally adaptive step sizes for Hamiltonian Monte Carlo methods
Exact reversible locally adaptive step sizes are introduced by Bou-Rabee et al. (2026) in the context of the No-U-Turn Hamiltonian Monte Carlo (HMC) sampler for the purpose of high performance HMC…CLASSO: a practical primal path-following algorithm for the constrained LASSO
We present CLASSO, a path-following method for the LASSO subject to general linear equality and inequality constraints.Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation
Verifier-guided inference-time scaling uses additional computation to score intermediate states and steer generation toward better outputs.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.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.Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels
Determinantal point processes (DPPs) model subset selection probability with repulsion, but many applications also exhibit attraction arising from pairwise relations or shared latent structures.