A Dual-Timescale Marked Point-Process and Empirical-Bayes Framework for Ranking Persistent Unmet Need in Crowdsourced Issue Observatories
- 📄 Complete Mathematical Specification & Whitepaper:
docs/QUIETFRAY_V0.1_SPECIFICATION.md - 🐍 Pure-Python
$O(1)$ Production Engine:sdk/mendseed_ai/quietflay.py - 🧪 Unit Test Suite:
tests/test_quietflay.py
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Sequential Hypothesis Testing: Formulates ranking as a continuous-time Sequential Probability Ratio Test (SPRT) between
$\mathcal{H}_1$ (Persistent Need) and$\mathcal{H}_0$ (Transient Viral / Sybil Noise). -
Dual-Kernel Marked Hawkes Process:
$\tau_f = 0.02\text{ days}$ ($28.8\text{ min}$ ) and$\tau_s = 3.0\text{ days}$ ($72.0\text{ hours}$ ). -
Exact
$O(1)$ Recurrence:$\sim 5.37\ \mu\text{s}$ per event update, zero historical event replays, floating-point drift$< 10^{-12}$ against 50-digit arbitrary-precision arithmetic. -
Zero Third-Party Dependencies: Pure Python core requiring only the standard library (
mathanddataclasses).