This repository features an analysis of Bayesian Optimization techniques applied to neutron scattering data. It explores the impact of prior knowledge on optimization, compares likelihood and posterior sampling methods, and applies Bayesian sampling to experimental data. Key libraries used include NumPy, SciPy, Matplotlib, Pandas, and Scikit-learn.
active 2024-07-14 → 2024-07-14 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-07-14 to 2024-07-14. Pushes: 4 total, peak 4 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| KayVeeZ | 4 | 4 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.