[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Hiyoko₿ [@hiyoko_peep](/creator/twitter/hiyoko_peep) on x 6803 followers Created: 2025-07-25 11:16:22 UTC The LPPL framework (✨ the magical formula for discovering the rhythm of bubbles 🔮) is almost complete! Right now, I’m running some light debugging on the final and most advanced feature — Bayesian inference 🌸. Everything else has passed testing ✨, so I’ll try running it on BTC. I’m heading out for a while, so I hope the analysis will progress in the meantime and give me results by tonight… 📊💭 I wrote this with a soft and fluffy vibe 🌿☺️✨ This is really just a hobby project, though… haha. --- The framework is built around the ultimate combo: “Ensemble Mode × 3-Level Hierarchical Checks” ✨ By fully powering up “3 Core Engines × X Levels of Verification,” it can unmask even the trickiest “false solutions” and discover the single most trustworthy answer — the LPPL parameters & the critical time 💡. --- The X Core Engines: 🟡 Dual Annealing (DA) 🟢 Covariance Matrix Adaptation Evolution Strategy (CMA-ES) 🟠 Adaptive Differential Evolution × Local Search (Adaptive DE × TRF) First, the CPU cores (the little “brains” of the computer) work like tiny master craftsmen, gathering every promising solution they can find ✨. --- Then begins the adventure of passing through X reliability gates 🐣 Here’s the big picture… Level X = Basic Screening (the exciting first review) Level X = Meta Review (group discussion time) Level X = Reproducibility Test (can we really get the same result?) (Research Stage) Bayesian Integration = Meta Analysis (crossing the finish line!) 🐣✨ --- A bit more detail… 🔽 We first take the elite candidate solutions with the smallest RMSE errors from the X engines, and pass them through X layers of hierarchical reliability checks: Level 1: Basic Check Any “off” parameters are eliminated here 🧐. Using the AIC score as a benchmark, we select only the top-performing candidates! Level 2: Practical Check We confirm whether the X engines produce “almost the same answer,” measuring the distance between parameters (their friendship level). Residual analysis is also performed to check for any strange patterns 🧐. Level 3: Stability Check (multi-horizon stability evaluation) We test reproducibility using 12-month, 6-month, and 3-month datasets. If the standard deviation of stays within XX days, we can say, “Stable and reliable!” 💎😊 --- At this point, the practical foundation of the framework is complete 💡. --- For Academic Research From here, the “winning solution” from Level 3, along with all training data, is used as the prior for Bayesian inference. This enables future predictions with confidence intervals 🍰 — just like a human mind weighing probabilities: “what % chance this will happen, and what % it won’t,” learning and refining as it goes ✨.  XXXXX engagements  **Related Topics** [inference](/topic/inference) [bitcoin](/topic/bitcoin) [coins layer 1](/topic/coins-layer-1) [coins bitcoin ecosystem](/topic/coins-bitcoin-ecosystem) [coins pow](/topic/coins-pow) [Post Link](https://x.com/hiyoko_peep/status/1948703868755316748)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Hiyoko₿ @hiyoko_peep on x 6803 followers
Created: 2025-07-25 11:16:22 UTC
The LPPL framework (✨ the magical formula for discovering the rhythm of bubbles 🔮) is almost complete! Right now, I’m running some light debugging on the final and most advanced feature — Bayesian inference 🌸. Everything else has passed testing ✨, so I’ll try running it on BTC. I’m heading out for a while, so I hope the analysis will progress in the meantime and give me results by tonight… 📊💭
I wrote this with a soft and fluffy vibe 🌿☺️✨ This is really just a hobby project, though… haha.
The framework is built around the ultimate combo: “Ensemble Mode × 3-Level Hierarchical Checks” ✨
By fully powering up “3 Core Engines × X Levels of Verification,” it can unmask even the trickiest “false solutions” and discover the single most trustworthy answer — the LPPL parameters & the critical time 💡.
The X Core Engines:
🟡 Dual Annealing (DA) 🟢 Covariance Matrix Adaptation Evolution Strategy (CMA-ES) 🟠 Adaptive Differential Evolution × Local Search (Adaptive DE × TRF)
First, the CPU cores (the little “brains” of the computer) work like tiny master craftsmen, gathering every promising solution they can find ✨.
Then begins the adventure of passing through X reliability gates 🐣
Here’s the big picture…
Level X = Basic Screening (the exciting first review)
Level X = Meta Review (group discussion time)
Level X = Reproducibility Test (can we really get the same result?)
(Research Stage) Bayesian Integration = Meta Analysis (crossing the finish line!) 🐣✨
A bit more detail… 🔽
We first take the elite candidate solutions with the smallest RMSE errors from the X engines, and pass them through X layers of hierarchical reliability checks:
Level 1: Basic Check Any “off” parameters are eliminated here 🧐. Using the AIC score as a benchmark, we select only the top-performing candidates!
Level 2: Practical Check We confirm whether the X engines produce “almost the same answer,” measuring the distance between parameters (their friendship level). Residual analysis is also performed to check for any strange patterns 🧐.
Level 3: Stability Check (multi-horizon stability evaluation) We test reproducibility using 12-month, 6-month, and 3-month datasets. If the standard deviation of stays within XX days, we can say, “Stable and reliable!” 💎😊
At this point, the practical foundation of the framework is complete 💡.
For Academic Research
From here, the “winning solution” from Level 3, along with all training data, is used as the prior for Bayesian inference. This enables future predictions with confidence intervals 🍰 — just like a human mind weighing probabilities: “what % chance this will happen, and what % it won’t,” learning and refining as it goes ✨.
XXXXX engagements
Related Topics inference bitcoin coins layer 1 coins bitcoin ecosystem coins pow