惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
MongoDB | Blog
MongoDB | Blog
Blog — PlanetScale
Blog — PlanetScale
腾讯CDC
U
Unit 42
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Apple Machine Learning Research
Apple Machine Learning Research
M
MIT News - Artificial intelligence
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
MyScale Blog
MyScale Blog
小众软件
小众软件
博客园 - 三生石上(FineUI控件)
N
Netflix TechBlog - Medium
阮一峰的网络日志
阮一峰的网络日志
博客园 - Franky
Recent Announcements
Recent Announcements
A
About on SuperTechFans
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
D
Docker
H
Hackread – Cybersecurity News, Data Breaches, AI and More

stat updates on arXiv.org

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
A New Algorithm for Totally Positive Approximations
[Submitted on 21 Jun 2026] · 2026-06-23 · via stat updates on arXiv.org

Statistics > Computation

arXiv:2606.22622 (stat)

[Submitted on 21 Jun 2026]

View PDF HTML (experimental)

Abstract:We revisit the problem of approximating a bivariate distribution with finite support by another such distribution which is totally positive or order two (TP2). Approximation is meant in a maximum likelihood sense.

Submission history

From: Lutz Duembgen [view email]
[v1] Sun, 21 Jun 2026 18:07:19 UTC (1,780 KB)

Current browse context:

stat.CO

Bookmark

BibSonomy Reddit

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Code, Data, Media

Code, Data and Media Associated with this Article

Demos

Demos

Related Papers

Recommenders and Search Tools

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.