Build Bayesian models and run MCMC sampling with PyMC.
npx skills add https://github.com/eyadsibai/ltk --skill pymc-eyadsibai
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/eyadsibai/ltk/tree/main/plugins/ltk-data/skills/pymc Command: npx skills add https://github.com/eyadsibai/ltk --skill pymc-eyadsibai
This Skill simplifies the process of building, fitting, and analyzing complex Bayesian statistical models using probabilistic programming.
Use the pymc skill to build a Bayesian linear regression model with priors on alpha and beta.
High-intent search queries and answers about installing and using this skill.
To build and fit Bayesian statistical models, you define priors and likelihoods to construct hierarchical models or logistic regression, then utilize Markov Chain Monte Carlo methods like NUTS for sampling and uncertainty quantification.
Probabilistic programming enables computing uncertainties by defining statistical models with prior distributions and performing posterior predictive checks to quantify parameter uncertainty after MCMC sampling.
Assess model convergence and fit for Bayesian hierarchical models by utilizing ArviZ to run model diagnostics, evaluate sampling traces, and compare probabilistic models against experimental data.
Yes, you need ArviZ for model diagnostics and comparison, and NumPy for data handling, because probabilistic programming for Bayesian modeling requires these dependencies to execute MCMC sampling successfully.
Yes, you can analyze experimental data with hierarchical models to understand group-level variations, utilizing Bayesian modeling to quantify uncertainty in parameters through prior and posterior predictive checks.
The best way to set up a Bayesian linear regression model is by defining prior distributions on parameters like alpha and beta, then using NUTS MCMC sampling to fit the model and evaluate convergence.
RamanEbrahimiConstructs and analyzes Bayesian models using PyMC.
viniruggeriBuild, fit, and compare Bayesian models with PyMC and ArviZ.
ovachieverBuild, fit, and evaluate Bayesian models with PyMC using MCMC sampling.
hxk622Build Bayesian models with PyMC for MCMC sampling and predictive checks.
