Myrdhin Zwetsloot developed core model selection and documentation features for the t-kist/Bayesian-Statistics-for-Astrophysics-2024 repository, focusing on reproducible AIC/BIC workflows and educator-ready Jupyter notebooks. They implemented Python-based data simulation, model fitting, and statistical analysis, enabling robust comparison of polynomial models using AIC and BIC metrics. Their work included LaTeX-formatted formulas, cross-referencing, and improved documentation clarity, supporting both research and teaching use cases. By resolving merge conflicts and refining notebook structure, Myrdhin enhanced code maintainability and onboarding. The depth of their contributions is reflected in clear, actionable guidance for model selection and improved communication of statistical methodology for astrophysics.
December 2024 focused on delivering a reproducible AIC/BIC model selection workflow and improving educator-ready documentation for Bayesian statistics in astrophysics. The work completed provides a solid, teaching-ready framework for model comparison and supports scalable experimentation with polynomial degrees. It also reinforces the team's ability to communicate methodology clearly through high-quality notebooks. Key features delivered: - AIC/BIC Model Selection Lab: core framework for comparing models across polynomial degrees, including data simulation, model fitting, AIC/BIC calculations, and visualization. - Notebook Documentation and Presentation Enhancements: improvements to notebook readability, notational clarity, and visuals for AIC/BIC lecture notes. Major bugs fixed: - Resolved merge conflicts and stabilized the AICvsBIC workflow to ensure reproducible execution paths. - Corrected chapter references and improved presentation-ready simulations for teaching materials. Overall impact and accomplishments: - Accelerated model-selection analytics for astrophysical data, enabling faster, data-driven decisions and more reliable model ranking. - Deliverables are ready for teaching demos and stakeholder reviews, increasing the team’s ability to demonstrate methodology and results. - Improved code quality and maintainability through explicit explanations and clearer notebook structure. Technologies/skills demonstrated: - Python for data simulation, model fitting, AIC/BIC calculations, and visualization. - Jupyter notebooks for interactive exploration and teaching materials. - Version control and collaboration practices (Git): debugging, merge conflict resolution, and documentation.
December 2024 focused on delivering a reproducible AIC/BIC model selection workflow and improving educator-ready documentation for Bayesian statistics in astrophysics. The work completed provides a solid, teaching-ready framework for model comparison and supports scalable experimentation with polynomial degrees. It also reinforces the team's ability to communicate methodology clearly through high-quality notebooks. Key features delivered: - AIC/BIC Model Selection Lab: core framework for comparing models across polynomial degrees, including data simulation, model fitting, AIC/BIC calculations, and visualization. - Notebook Documentation and Presentation Enhancements: improvements to notebook readability, notational clarity, and visuals for AIC/BIC lecture notes. Major bugs fixed: - Resolved merge conflicts and stabilized the AICvsBIC workflow to ensure reproducible execution paths. - Corrected chapter references and improved presentation-ready simulations for teaching materials. Overall impact and accomplishments: - Accelerated model-selection analytics for astrophysical data, enabling faster, data-driven decisions and more reliable model ranking. - Deliverables are ready for teaching demos and stakeholder reviews, increasing the team’s ability to demonstrate methodology and results. - Improved code quality and maintainability through explicit explanations and clearer notebook structure. Technologies/skills demonstrated: - Python for data simulation, model fitting, AIC/BIC calculations, and visualization. - Jupyter notebooks for interactive exploration and teaching materials. - Version control and collaboration practices (Git): debugging, merge conflict resolution, and documentation.
In November 2024, I delivered targeted features and quality improvements for the Bayesian-Statistics-for-Astrophysics project (t-kist/Bayesian-Statistics-for-Astrophysics-2024), focusing on enhanced model interpretation, notebook clarity, and documentation accuracy. The work improves actionable guidance for model selection and accelerates researcher onboarding through clearer explanations, robust formulas, and polished notes across notebooks and docs.
In November 2024, I delivered targeted features and quality improvements for the Bayesian-Statistics-for-Astrophysics project (t-kist/Bayesian-Statistics-for-Astrophysics-2024), focusing on enhanced model interpretation, notebook clarity, and documentation accuracy. The work improves actionable guidance for model selection and accelerates researcher onboarding through clearer explanations, robust formulas, and polished notes across notebooks and docs.
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