Generate Markov null models with explicit order for trajectory TDA permutation tests.
npx skills add https://github.com/stephendor/TDL --skill markov-null-design
Please help me install this Agent Skill. Skill: markov-null-design Source: https://github.com/stephendor/TDL/tree/main/.agents/skills/markov-null-design Command: npx skills add https://github.com/stephendor/TDL --skill markov-null-design
This Skill helps in designing and scaffolding Markov null models for the trajectory TDA permutation pipeline, ensuring explicit Markov order and automating the permutation test call and result logging.
permutation_test_trajectories call.Run /markov-null-design markov 2 to scaffold a Markov-2 null model.
High-intent search queries and answers about installing and using this skill.
You can design a Markov null model for trajectory topology permutation tests by generating a model with an explicit Markov order, which scaffolds the necessary structure for your TDA analysis pipeline.
You can automate permutation test calls for trajectory TDA analysis by using a skill that wraps the permutation_test_trajectories function, automatically invoking it and logging the results along with the random seeds.
A Markov null model in TDA permutation pipelines is used to generate randomized trajectory data preserving Markov properties, serving as a statistical baseline to test whether observed trajectory topology is statistically significant.
Yes, you can specify the Markov order when generating a null model for trajectory analysis by providing the desired order value as a command argument, such as setting it to 2 for a Markov-2 model.
No, manual seed logging is not required because the permutation test automation includes a stub for logging results and provides a reminder to log seeds, ensuring reproducibility of your trajectory TDA analysis.
Markov null models for trajectory topology analysis enforce an explicit Markov order, meaning they assume trajectory dependencies only exist up to that specified order and may not capture complex long-range dependencies present in your data.
jackjin1997Generate and upload LangSmith trace-based evaluation datasets for LangChain agents.
stephendorValidate topological data analysis results against benchmarks and internal consistency checks.
bettercallfanPredict the next location from historical user trajectories using a Markov chain model.
modus-biGenerate optimized SQL queries for BI dashboards with Russian field labels.