t-kist/Bayesian-Statistics-for-Astrophysics-2024
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data Science EducationData VisualizationJupyter NotebookPythonStatistical AnalysisStatistics
Thain007
PROFILE
Thain007
Worked on the t-kist/Bayesian-Statistics-for-Astrophysics-2024 repository to enhance the reliability of Bayesian statistics teaching materials for astrophysics. Developed an expanded confidence interval coverage feature in the lecture notes, introducing bootstrap-based interval estimation and a Python demonstration for confidence intervals under a normal distribution. Refined the explanation of gamma-confidence intervals by clarifying the relationship between quantiles and alpha, and consolidated technical content to improve clarity. Utilized Jupyter Notebook and Python to deliver practical examples and statistical analysis, focusing on data science education and technical writing. The work improved uncertainty quantification guidance for students without addressing major bugs during the period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
101
Activity Months1
Your Network
14 peopleSame Organization
@vuw.leidenuniv.nl3
Shared Repositories
11
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RevennaMember
Work History
November 2024
3 Commits • 1 Features
Nov 1, 20242024-11 monthly summary: Delivered an enhanced Confidence Interval (CI) coverage feature for Bayesian statistics teaching materials in astrophysics. Implemented major feature to strengthen CI coverage in Lecture Notes, including bootstrap-based interval estimation discussion, a Python demonstration for CIs under a normal distribution, and refined gamma-confidence explanations with explicit alpha-quantile relationships. Minor polish and corrections across the chapter; no major bugs fixed this month. This work improves teaching reliability and practical uncertainty quantification for astrophysical analyses.
3 Commits • 1 Features
Nov 1, 20242024-11 monthly summary: Delivered an enhanced Confidence Interval (CI) coverage feature for Bayesian statistics teaching materials in astrophysics. Implemented major feature to strengthen CI coverage in Lecture Notes, including bootstrap-based interval estimation discussion, a Python demonstration for CIs under a normal distribution, and refined gamma-confidence explanations with explicit alpha-quantile relationships. Minor polish and corrections across the chapter; no major bugs fixed this month. This work improves teaching reliability and practical uncertainty quantification for astrophysical analyses.
November 2024
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data Science EducationData VisualizationJupyter NotebookPythonStatistical AnalysisStatisticsTechnical Writing
Repositories Contributed To
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