arXiv stat.CO Computation

Sanket Agrawal, Sebastiano Grazzi, Gareth O. Roberts: On micromodes in Bayesian posterior distributions and their implications for MCMC https://arxiv.org/abs/2602.06931 https://arxiv.org/pdf/2602.06931 https://arxiv.org/html/2602.06931

Alexandre Chotard (LISIC): Ergodicity of an Adaptive MCMC Sampler under a Probability Bound https://arxiv.org/abs/2602.06568 https://arxiv.org/pdf/2602.06568 https://arxiv.org/html/2602.06568

JoonHo Lee: Design-Conditional Prior Elicitation for Dirichlet Process Mixtures: A Unified Framework for Cluster Counts and Weight Control https://arxiv.org/abs/2602.06301 https://arxiv.org/pdf/2602.06301 https://arxiv.org/html/2602.06301

[2026-02-09 Mon (UTC), no new articles found for statCO Computation]

Magid Sabbagh, David A. Stephens: Posterior Uncertainty for Targeted Parameters in Bayesian Bootstrap Procedures https://arxiv.org/abs/2602.02216 https://arxiv.org/pdf/2602.02216 https://arxiv.org/html/2602.02216

Matias D. Cattaneo, Boris Shigida: The Effect of Mini-Batch Noise on the Implicit Bias of Adam https://arxiv.org/abs/2602.01642 https://arxiv.org/pdf/2602.01642 https://arxiv.org/html/2602.01642

Leonardo Ferreira Guilhoto, Akshat Kaushal, Paris Perdikaris: Multimodal Scientific Learning Beyond Diffusions and Flows https://arxiv.org/abs/2602.00960 https://arxiv.org/pdf/2602.00960 https://arxiv.org/html/2602.00960

Alain Durmus, Maxence Noble, Thibaut Pellerin: Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants https://arxiv.org/abs/2602.00641 https://arxiv.org/pdf/2602.00641 https://arxiv.org/html/2602.00641

Xuefei Cao, Shijia Wang, Yongdao Zhou: A multifidelity approximate Bayesian computation with pre-filtering https://arxiv.org/abs/2602.01770 https://arxiv.org/pdf/2602.01770 https://arxiv.org/html/2602.01770

Beatrice Franzolini, Francesco Gaffi: Complexity bounds for Dirichlet process slice samplers https://arxiv.org/abs/2602.00878 https://arxiv.org/pdf/2602.00878 https://arxiv.org/html/2602.00878

Xinyi Pei, Minhyeok Kim, Vinayak Rao: Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion https://arxiv.org/abs/2602.00512 https://arxiv.org/pdf/2602.00512 https://arxiv.org/html/2602.00512

[2026-02-03 Tue (UTC), 3 new articles found for statCO Computation]

Nicolas Bianco, Lorenzo Cappello: Computationally efficient segmentation for non-stationary time series with oscillatory patterns https://arxiv.org/abs/2601.22999 https://arxiv.org/pdf/2601.22999 https://arxiv.org/html/2601.22999

Ana Arribas-Gil, Sara L\'opez-Pintado: Depth-based estimation for multivariate functional data with phase variability https://arxiv.org/abs/2601.22884 https://arxiv.org/pdf/2601.22884 https://arxiv.org/html/2601.22884

Babak Shahbaba, Zahra Moslemi: Neural-Inspired Posterior Approximation (NIPA) https://arxiv.org/abs/2601.22539 https://arxiv.org/pdf/2601.22539 https://arxiv.org/html/2601.22539

Jing Jia, Wei Yuan, Sifan Liu, Liyue Shen, Guanyang Wang: Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance https://arxiv.org/abs/2601.22443 https://arxiv.org/pdf/2601.22443 https://arxiv.org/html/2601.22443

Chaoyi Lu, Riccardo Rastelli: Mixed Latent Position Cluster Models for Networks https://arxiv.org/abs/2601.22380 https://arxiv.org/pdf/2601.22380 https://arxiv.org/html/2601.22380

Aditya Narayan Ravi, Snehal Vadvalkar, Abhishek Pandey, Ilan Shomorony: Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling https://arxiv.org/abs/2601.22331 https://arxiv.org/pdf/2601.22331 https://arxiv.org/html/2601.22331

David Yallup, Namu Kroupa, Will Handley: Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference https://arxiv.org/abs/2601.23252 https://arxiv.org/pdf/2601.23252 https://arxiv.org/html/2601.23252

Rob Cornish, Andi Q. Wang: A categorical account of the Metropolis-Hastings algorithm https://arxiv.org/abs/2601.22911 https://arxiv.org/pdf/2601.22911 https://arxiv.org/html/2601.22911

Christina Schenk, Ignacio Romero: A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences https://arxiv.org/abs/2601.22890 https://arxiv.org/pdf/2601.22890 https://arxiv.org/html/2601.22890

Yiting Duan, Zhikun Zhang, Yi Guo: Wasserstein Geometry of Information Loss in Nonlinear Dynamical Systems https://arxiv.org/abs/2601.22814 https://arxiv.org/pdf/2601.22814 https://arxiv.org/html/2601.22814

[2026-02-02 Mon (UTC), 4 new articles found for statCO Computation]

James Cuin, Davide Carbone, Yanbo Tang, O. Deniz Akyildiz: Efficient Stochastic Optimisation via Sequential Monte Carlo https://arxiv.org/abs/2601.22003 https://arxiv.org/pdf/2601.22003 https://arxiv.org/html/2601.22003

Shuigen Liu, Xin T. Tong: Spectral Gap of Metropolis Algorithms for Non-smooth Distributions under Isoperimetry https://arxiv.org/abs/2601.21763 https://arxiv.org/pdf/2601.21763 https://arxiv.org/html/2601.21763

Dongyue Xie, Wanrong Zhu, Matthew Stephens: A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression https://arxiv.org/abs/2601.21217 https://arxiv.org/pdf/2601.21217 https://arxiv.org/html/2601.21217

Youguang Chen, George Biros: Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators https://arxiv.org/abs/2601.20888 https://arxiv.org/pdf/2601.20888 https://arxiv.org/html/2601.20888

Augusto Fasano, Giovanni Rebaudo: Mean-field Variational Bayes for Sparse Probit Regression https://arxiv.org/abs/2601.21765 https://arxiv.org/pdf/2601.21765 https://arxiv.org/html/2601.21765

Hugo Morvan, Jonas Agholme, Bjorn Eliasson, Katarina Olofsson, Ludger Grote, Fredrik Iredahl, Oleg Sysoev: bigMICE: Multiple Imputation of Big Data https://arxiv.org/abs/2601.21613 https://arxiv.org/pdf/2601.21613 https://arxiv.org/html/2601.21613

[2026-01-30 Fri (UTC), 2 new articles found for statCO Computation]