Cubature-based uncertainty estimation for nonlinear regression models https://arxiv.org/abs/2409.08756 #stat.ME #math.NA #cs.NA
Spatial Deep Convolutional Neural Networks https://arxiv.org/abs/2409.07559 #stat.ME #stat.AP
Debiased high-dimensional regression calibration for errors-in-variables log-contrast models https://arxiv.org/abs/2409.07568 #stat.ME #stat.ML
Determining number of factors under stability considerations https://arxiv.org/abs/2409.07617 #stat.ME
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems https://arxiv.org/abs/2409.07637 #stat.ML #stat.AP #cs.AI #cs.LG
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks https://arxiv.org/abs/2409.07652 #stat.ML #math.ST #stat.TH #cs.LG
Unsupervised anomaly detection in spatio-temporal stream network sensor data https://arxiv.org/abs/2409.07667 #stat.AP
Dataset-Free Weight-Initialization on Restricted Boltzmann Machine https://arxiv.org/abs/2409.07708 #cond-mat.dis-nn #stat.ML #cs.LG
A model-based approach for clustering binned data https://arxiv.org/abs/2409.07738 #stat.ME #math.ST #stat.TH
A Stochastic Weather Model: A Case of Bono Region of Ghana https://arxiv.org/abs/2409.06731 #stat.AP #math.PR
Kramnik vs Nakamura: A Chess Scandal https://arxiv.org/abs/2409.06739 #stat.AP
Learning Deep Kernels for Non-Parametric Independence Testing https://arxiv.org/abs/2409.06890 #stat.ML #cs.LG
k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation https://arxiv.org/abs/2409.06938 #stat.ML #cs.LG
Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions https://arxiv.org/abs/2409.06960 #physics.data-an #stat.ML #stat.AP #cs.LG
A Practical Theory of Generalization in Selectivity Learning https://arxiv.org/abs/2409.07014 #stat.ML #cs.DB #cs.LG
Clustered Factor Analysis for Multivariate Spatial Data https://arxiv.org/abs/2409.07018 #stat.ME
From optimal score matching to optimal sampling https://arxiv.org/abs/2409.07032 #stat.ML #cs.LG
Identifiability of Polynomial Models from First Principles and via a Gr\"obner Basis Approach https://arxiv.org/abs/2409.07062 #math.ST #stat.TH
I post the feed of the arXiv Statistics.
#Statistics #Stats #Mathematics #Math #Maths #Science #arXiv #News #PeerReview