Build and fit Bayesian models with PyMC using NUTS and ADVI inference.
npx skills add https://github.com/must1f/Dissertaion-Project --skill pymc-must1f
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/must1f/Dissertaion-Project/tree/main/.agents/skills/pymc Command: npx skills add https://github.com/must1f/Dissertaion-Project --skill pymc-must1f
PyMC provides a principled framework for building and evaluating Bayesian models, enabling robust uncertainty quantification in data analysis.
Define a small PyMC model, sample from the posterior, and inspect the results.
High-intent search queries and answers about installing and using this skill.
To build Bayesian models with PyMC, define your probabilistic model using its modern API, fit it using NUTS or ADVI, and quantify uncertainty in parameter estimates and forecasts across domains like finance and science.
Posterior predictive checks and model comparison using LOO and WAIC are performed by sampling the posterior with PyMC and passing the results to ArviZ for diagnostics and information criteria calculation.
Yes, this Skill supports building and fitting hierarchical and time series Bayesian models, utilizing named dimensions and coordinates for scalable and readable model definitions.
The best way to diagnose MCMC inference results is by using ArviZ, which is fully integrated to provide comprehensive diagnostics for your PyMC posterior samples and ensure robust model evaluation.
While PyMC handles the NUTS and ADVI variational inference execution, ArviZ is required as a dependency to inspect the results, conduct diagnostics, and perform posterior predictive checks.
Yes, PyMC is explicitly designed for Bayesian forecasting and data analysis in social science, allowing you to apply robust uncertainty quantification to your models and parameter estimates.
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