Construct Bayesian models and run probabilistic inference with PyMC.
npx skills add https://github.com/mkurman/tamux --skill pymc-mkurman
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/pymc Command: npx skills add https://github.com/mkurman/tamux --skill pymc-mkurman
PyMC enables robust Bayesian modeling and probabilistic programming to quantify uncertainty in data-driven decisions.
Install PyMC, load your dataset, and run the provided templates to build a basic Bayesian model and generate diagnostics.
High-intent search queries and answers about installing and using this skill.
Bayesian inference for hierarchical models uses PyMC's MCMC/NUTS samplers to estimate posterior distributions, quantify uncertainty, and apply probabilistic programming to data analysis across continuous and hierarchical datasets.
Comparing Bayesian models using LOO and WAIC involves running posterior predictive checks and diagnostics with ArviZ to evaluate model fit, quantify uncertainty, and validate probabilistic inference results.
PyMC works with pandas and numpy for data analysis, enabling Bayesian modeling and probabilistic inference directly on real datasets loaded via these libraries to quantify uncertainty in data-driven decisions.
Prior and posterior predictive checks for Bayesian models validate assumptions by simulating data from priors and posteriors, using PyMC templates and ArviZ diagnostics to assess model fit and uncertainty.
Bayesian regression templates from PyMC apply to small and large datasets, providing ready-made linear and hierarchical model structures that accelerate Bayesian inference and uncertainty quantification across varying data scales.
Probabilistic programming for causal modeling and experimental design uses Bayesian inference to quantify uncertainty in data-driven decisions, applying PyMC to test hypotheses and estimate effects across complex relationships.
StarlitnightlyPerform statistical tests and corrections on datasets using SciPy, statsmodels, and NumPy.
jg-youGenerate ArviZ diagnostic and substantive figures for Bayesian analyses.
SologaBuild and validate Bayesian models with PyMC using MCMC and variational inference.
biomaps-infraBuild Bayesian models with PyMC and run MCMC sampling with NUTS.
