<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Stochastic Processes on C.CUI's Log</title><link>https://cuicaihao.github.io/tags/stochastic-processes/</link><description>Recent content in Stochastic Processes on C.CUI's Log</description><generator>Hugo</generator><language>en-AU</language><lastBuildDate>Sat, 24 Oct 2026 07:00:00 +1100</lastBuildDate><atom:link href="https://cuicaihao.github.io/tags/stochastic-processes/index.xml" rel="self" type="application/rss+xml"/><item><title>Retail Demand Forecasting with Graph Neural Networks: Mathematical Principles, Controlled Experiments, and Production Boundaries</title><link>https://cuicaihao.github.io/posts/2026-10-24-fsm-stochastic-gnn-retail-forecast/</link><pubDate>Sat, 24 Oct 2026 07:00:00 +1100</pubDate><guid>https://cuicaihao.github.io/posts/2026-10-24-fsm-stochastic-gnn-retail-forecast/</guid><description>Starting from stochastic state changes and information diffusion on graphs, this article explains when graph structure improves retail demand forecasting, compares local, neighbourhood, and graph models in a synthetic 2-warehouse, 10-store network, and examines why algorithms that win on paper can fail in production execution.</description></item></channel></rss>