m3taversal
be8ff41bfe
Wrote sourced_from: into 414 claim files pointing back to their origin source. Backfilled claims_extracted: into 252 source files that were processed but missing this field. Matching uses author+title overlap against claim source: field, validated against 296 known-good pairs from existing claims_extracted. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
36 lines
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2.4 KiB
Markdown
36 lines
No EOL
2.4 KiB
Markdown
---
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type: claim
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domain: internet-finance
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description: "CIATA method models time-varying bursty arrivals through combined rate and variance parameters"
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confidence: proven
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source: "Liu et al. (NC State), 'Modeling and Simulation of Nonstationary Non-Poisson Arrival Processes' (2019)"
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created: 2026-03-11
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sourced_from:
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- inbox/archive/internet-finance/2019-00-00-liu-modeling-nonstationary-non-poisson-arrival-processes.md
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---
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# Nonstationary non-Poisson arrival modeling requires rate function plus dispersion ratio to capture burstiness
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Standard Poisson process assumptions break down when arrivals exhibit correlation and burstiness. The CIATA (Combined Inversion-and-Thinning Approach) method models arrival processes through two parameters: a rate function λ(t) capturing time-varying intensity, and an asymptotic variance-to-mean (dispersion) ratio capturing burstiness beyond what the rate alone predicts.
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This two-parameter approach is necessary because time-varying rate alone cannot capture the correlation structure of bursty arrivals. A process with constant high variance but varying rate behaves fundamentally differently from a Poisson process with the same rate function.
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## Evidence
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Liu et al. demonstrate that CIATA models "target arrival processes via rate function + dispersion ratio — captures both time-varying intensity and burstiness." The paper shows that "replacing a time-varying arrival rate with a constant (max or average) leads to systems being badly understaffed or overstaffed," proving that rate variation alone is insufficient.
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The Markov-Modulated Poisson Process (MMPP) framework provides the theoretical foundation: "arrival rate switches between states governed by a hidden Markov chain — natural model for 'bursty then quiet' patterns." This captures the correlation structure that pure rate functions miss.
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## Relevance to Internet Finance
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This modeling framework directly applies to capital formation pipelines where research sessions create bursts of 10-20 source arrivals followed by quiet periods of 0-2 per day. The hidden state (research session active vs. inactive) governs the arrival rate, making this a textbook MMPP application.
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Capacity planning based on average arrival rates will systematically fail for such processes, leading to either chronic congestion during bursts or wasteful overcapacity during quiet periods.
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---
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Relevant Notes:
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- domains/internet-finance/_map
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Topics:
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- core/mechanisms/_map |