stan-dev/math
Jul 2025 – Oct 2025
3 Months active
Languages Used
C++
Technical Skills
C++ DevelopmentProbability DistributionsStatistical ModelingUnit TestingMathematical LibrariesSoftware Testing
Ling Zhi
PROFILE
Ling Zhi
Lingzhi contributed to the stan-dev/math repository by developing and refining statistical distribution functions in C++. Over three months, Lingzhi implemented the Yule-Simon distribution’s log PMF and LCCDF, focusing on robust input handling, domain checks, and comprehensive unit testing to ensure correctness across scalar and sequence inputs. The work included direct assignment of partial derivatives for improved numerical accuracy and performance, particularly in autodiff-heavy workflows. By introducing autodiff-aware derivative gating, Lingzhi reduced unnecessary computations and prevented spurious gradients. The technical approach emphasized code clarity, maintainability, and testability, demonstrating depth in probability distributions, mathematical libraries, and software testing practices.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
327
Activity Months3
Your Network
14 people
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for stan-dev/math focusing on correctness and performance in autodiff paths. Delivered update improves derivative gating for Yule-Simon LCCDF to compute partial derivatives only when alpha requires differentiation, reducing unnecessary work and ensuring correct derivatives for autodiff usage. Commit 94dda8f755926433e6d93834919df69c5543d4d6 updated stan/math/prim/prob/yule_simon_lccdf.hpp. This work enhances reliability of probabilistic computations involving LCCDF and supports heavier autodiff workloads.
1 Commits
Oct 1, 2025October 2025 monthly summary for stan-dev/math focusing on correctness and performance in autodiff paths. Delivered update improves derivative gating for Yule-Simon LCCDF to compute partial derivatives only when alpha requires differentiation, reducing unnecessary work and ensuring correct derivatives for autodiff usage. Commit 94dda8f755926433e6d93834919df69c5543d4d6 updated stan/math/prim/prob/yule_simon_lccdf.hpp. This work enhances reliability of probabilistic computations involving LCCDF and supports heavier autodiff workloads.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on delivering technical capabilities that drive accurate probabilistic modeling and code quality improvements in Stan Math. The principal delivery this month was a robust Yule-Simon LCCDF implementation with derivative refinements, supported by expanded test coverage and code updates to improve readability and maintainability. No major user-facing bug fixes were required; the work centered on correctness, testability, and future-proofing for distribution modeling in production workflows.
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on delivering technical capabilities that drive accurate probabilistic modeling and code quality improvements in Stan Math. The principal delivery this month was a robust Yule-Simon LCCDF implementation with derivative refinements, supported by expanded test coverage and code updates to improve readability and maintainability. No major user-facing bug fixes were required; the work centered on correctness, testability, and future-proofing for distribution modeling in production workflows.
July 2025
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary focusing on delivering robust statistical functionality in stan-dev/math for 2025-07. The effort centered on implementing the Yule-Simon distribution log PMF (lpmf) with comprehensive input handling, unit tests, and accuracy improvements, along with clear traceability to commits.
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary focusing on delivering robust statistical functionality in stan-dev/math for 2025-07. The effort centered on implementing the Yule-Simon distribution log PMF (lpmf) with comprehensive input handling, unit tests, and accuracy improvements, along with clear traceability to commits.
July 2025
Activity
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Quality Metrics
Correctness92.0%
Maintainability96.0%
Architecture92.0%
Performance88.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ DevelopmentMathematical LibrariesProbability DistributionsSoftware TestingStatistical ModelingUnit Testing
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