t-kist/Bayesian-Statistics-for-Astrophysics-2024
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Astrophysics Data AnalysisBayesian StatisticsData CleaningData VisualizationDocumentationJupyter Notebook
[NotBenthe]
PROFILE
[notbenthe]
Over two months, NotBenthe enhanced the t-kist/Bayesian-Statistics-for-Astrophysics-2024 repository by developing Bayesian outlier rejection materials and delivering MCMC tooling for astrophysics data analysis. They improved Jupyter Notebook workflows by clarifying prior probabilities, modeling outliers with Gaussian distributions, and integrating posterior calculations, all aimed at increasing teaching clarity and reproducibility. NotBenthe introduced new outlier visualizations and refactored notebooks for better readability. In December, they implemented a run_mcmc function, improved MCMC result plotting, and fixed statistical notation and MLE calculation bugs. Their work demonstrated depth in Bayesian statistics, scientific computing, and technical writing, resulting in more reliable and maintainable course materials.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
2
Lines of code
1,559
Activity Months2
Your Network
15 peopleSame Organization
@users.noreply.github.com]4
Work History
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for t-kist/Bayesian-Statistics-for-Astrophysics-2024. Focused on delivering data-driven analytics capabilities, improving reliability, and enhancing reproducibility in Bayesian astrophysics workflows. Highlights include delivery of MCMC tooling and visualization enhancements, and targeted bug fixes to ensure correctness of statistical notation and MLE calculations.
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for t-kist/Bayesian-Statistics-for-Astrophysics-2024. Focused on delivering data-driven analytics capabilities, improving reliability, and enhancing reproducibility in Bayesian astrophysics workflows. Highlights include delivery of MCMC tooling and visualization enhancements, and targeted bug fixes to ensure correctness of statistical notation and MLE calculations.
December 2024
November 2024
4 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for t-kist/Bayesian-Statistics-for-Astrophysics-2024. Delivered feature enhancements to Bayesian outlier rejection materials, including notebook enhancements, clearer priors, Gaussian modeling of outliers, posterior integration, notebook cleanup, and a new outlier visualization. All changes focused on improving teaching clarity, reproducibility, and maintainability of the materials. No critical bugs reported; the feature work complemented ongoing coursework materials and maintained code quality.
November 2024
4 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for t-kist/Bayesian-Statistics-for-Astrophysics-2024. Delivered feature enhancements to Bayesian outlier rejection materials, including notebook enhancements, clearer priors, Gaussian modeling of outliers, posterior integration, notebook cleanup, and a new outlier visualization. All changes focused on improving teaching clarity, reproducibility, and maintainability of the materials. No critical bugs reported; the feature work complemented ongoing coursework materials and maintained code quality.
Activity
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Quality Metrics
Correctness88.6%
Maintainability88.6%
Architecture85.6%
Performance80.0%
AI Usage28.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Astrophysics Data AnalysisBayesian InferenceBayesian StatisticsData CleaningData VisualizationDocumentationJupyter NotebookJupyter NotebooksMCMCOutlier DetectionOutlier RejectionScientific ComputingStatistical ModelingTechnical Writing
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