Build Bayesian models with PyMC for MCMC sampling and predictive checks.
npx skills add https://github.com/hxk622/TokenDance --skill pymc-hxk622
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/research-tools/pymc Command: npx skills add https://github.com/hxk622/TokenDance --skill pymc-hxk622
This Skill empowers users to build, fit, and validate complex Bayesian statistical models, enabling sophisticated data analysis and uncertainty quantification.
Use the pymc skill to build a Bayesian linear regression model for the provided dataset.
High-intent search queries and answers about installing and using this skill.
You can perform Bayesian inference by constructing hierarchical models and executing MCMC sampling with NUTS. This approach quantifies uncertainty and estimates parameters for complex datasets like clinical trial results.
MCMC sampling with NUTS provides exact posterior distributions through iterative sampling, while variational inference approximates distributions faster. This Skill supports both mechanisms for Bayesian inference depending on your accuracy and performance needs.
Yes, you validate Bayesian models using prior and posterior predictive checks. This Skill enables comprehensive model validation to ensure your probabilistic inferences accurately represent the observed data.
Estimate treatment effects with uncertainty in clinical trial data by applying Bayesian regression models. This Skill facilitates constructing statistical models that quantify uncertainty for principled inference.
Probabilistic programming with this Skill requires no explicit dependencies. It internally handles Bayesian modeling and inference tasks, allowing you to build and validate models without managing external packages.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
lohasleApply descriptive statistics, trend analysis, outlier detection, and hypothesis testing to datasets.
xjtulycBuild Bayesian models with PyMC 5.x and ArviZ using NUTS sampling.
jimmc414Build, fit, and validate Bayesian models with PyMC.
