Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R

2026-08-26

AI Summary

本文介绍了R软件包steinsampling，通过核化Stein差异方法解决模型评估和样本近似问题，适用于独立或序列依赖观测的拟合优度测试及采样工具。

Abstract

Stein's method constructs computable discrepancies between a target distribution and a candidate distribution without requiring the target distribution's normalizing constant. These discrepancies support goodness-of-fit tests for model assessment as well as sampling tools for empirical approximation. The R package steinsampling provides the first unified R workflow for applying score-based Stein methods to kernel goodness-of-fit testing of independent or serially dependent observations, point transport, greedy point construction, and sample compression. High-level functions carry out each task in a single call, while the kernel, calibration, optimization, and transition components are provided separately so that users can replace any one of them. A single score and kernel setup can therefore be reused across sampling and testing, making these methods easier to reproduce, compare, and extend.

Problem

• Stein's method
• goodness-of-fit tests
• kernel discrepancy
• sampling tools

Innovation

• Kernelized Stein Discrepancy
• Goodness-of-Fit Tests
• Stein Sampling
• R Package
• Score-Based Methods