Build and validate Bayesian models with PyMC using MCMC sampling.
npx skills add https://github.com/Yezez9/Research-Agent --skill pymc-yezez9
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/pymc Command: npx skills add https://github.com/Yezez9/Research-Agent --skill pymc-yezez9
This Skill empowers users to build, fit, and validate sophisticated Bayesian statistical models, enabling robust inference and uncertainty quantification for complex data analysis tasks.
Use the pymc skill to build a Bayesian linear regression model for the provided data.
High-intent search queries and answers about installing and using this skill.
Bayesian hierarchical modeling and regression analysis are constructed by defining prior distributions and likelihood functions, then fitting the model using MCMC NUTS sampling or variational inference to quantify uncertainty.
Probabilistic programming defines statistical models programmatically to perform Bayesian inference, needed when quantifying uncertainty or modeling complex dependencies that traditional frequentist approaches cannot capture.
Bayesian model validation involves performing prior and posterior predictive checks, analyzing diagnostics like R-hat, ESS, and divergences, and comparing models using LOO or WAIC to ensure robust inference.
Time series modeling and customer behavior prediction are supported through Bayesian approaches, allowing you to learn population-level trends while accounting for individual variations to provide reliable predictions with confidence intervals.
The Skill works seamlessly with Python data tools, depending on numpy and pandas for data manipulation, matplotlib for visualization, and arviz for diagnostic analysis of Bayesian models.
Comparing Bayesian models for uncertainty quantification is best done using information criteria like LOO (Leave-One-Out cross-validation) and WAIC, which evaluate predictive accuracy and help select the most robust model.
xiechyParallelize pandas and NumPy workflows with Dask DataFrames and Arrays.
robinbarvaagGenerate interactive scientific and statistical data visualizations with Python Plotly.
ovachieverBuild, fit, and evaluate Bayesian models with PyMC using MCMC sampling.
nferrer-devPerform Bayesian modeling with PyMC and ArviZ for MCMC sampling and diagnostics.
