Build Bayesian models with PyMC and run MCMC sampling with NUTS.
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pymc
Please help me install this Agent Skill. Skill: lib-pymc Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-pymc Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-pymc
This Skill empowers users to build, fit, and validate complex Bayesian statistical models, enabling robust probabilistic programming and inference for data analysis.
Use the lib-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.
You can build a Bayesian hierarchical model by defining probabilistic structures and priors using PyMC. This Skill enables constructing hierarchical regressions, time series models, and analyzing experimental data with full uncertainty quantification.
The best way to perform MCMC sampling with NUTS is using this Skill's PyMC integration. It facilitates robust probabilistic programming, enabling efficient MCMC sampling and variational inference for complex statistical models.
Yes, Bayesian modeling quantifies uncertainty in predictive models by sampling posterior distributions. This Skill supports building predictive models for scientific research and performing complex simulations to assess data reliability.
You assess model convergence and fit using integrated ArviZ diagnostic tools. This Skill provides comprehensive model validation capabilities, allowing you to evaluate sampling reliability and assess the overall quality of your Bayesian inference.
Yes, this Skill supports variational inference through ADVI alongside MCMC sampling. It enables scalable Bayesian inference for data analysis, allowing you to fit complex probabilistic models efficiently when full MCMC is computationally prohibitive.
mkurmanConstruct Bayesian models and run probabilistic inference with PyMC.
jg-youGenerate ArviZ diagnostic and substantive figures for Bayesian analyses.
xjtulycBuild Bayesian models with PyMC 5.x and ArviZ using NUTS sampling.
ZurybrGuide MCMC sampling with RStan or PyStan for Bayesian models.
