Build Bayesian models with PyMC and NumPyro for MCMC inference.
npx skills add https://github.com/tondevrel/scientific-agent-skills --skill pymc
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/pymc%20%26%20numpyro Command: npx skills add https://github.com/tondevrel/scientific-agent-skills --skill pymc
This Skill empowers users to perform advanced statistical modeling and inference using Bayesian methods, enabling a deeper understanding of uncertainty and relationships within data than traditional approaches.
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 inference quantifies uncertainty by estimating full posterior distributions. This Skill uses PyMC and NumPyro to perform MCMC sampling and variational inference, providing credible intervals and probability statements for your parameters.
Building hierarchical models for nested data structures requires declarative probabilistic programming. This Skill supports defining complex hierarchical models to accurately capture variations across grouped data, such as students within schools.
Yes, you can use NumPyro and JAX for MCMC sampling. This Skill integrates PyMC with NumPyro and JAX dependencies to execute state-of-the-art Markov Chain Monte Carlo NUTS samplers for efficient posterior inference.
Running a Bayesian A/B test involves calculating the posterior probability of treatment effects. This Skill facilitates Bayesian A/B testing, allowing you to make clear, data-driven probability statements rather than relying solely on p-values.
Model comparison evaluates competing Bayesian models using specific statistical criteria. This Skill integrates with ArviZ to compute WAIC and LOO-CV metrics, enabling you to select the best model based on predictive accuracy.
Yes, you need a solid understanding of Bayesian principles and statistical modeling. This Skill requires prerequisite knowledge to effectively specify declarative models, perform inference, and conduct posterior predictive checks.
jaechang-hitsPerform Bayesian modeling workflows with PyMC and ArviZ.
eyadsibaiBuild Bayesian models and run MCMC sampling with PyMC.
tassiovaleConstruct and analyze Bayesian models with PyMC for probabilistic inference.
Ritabrata-ChakrabortyBuild and analyze Bayesian models with PyMC's MCMC sampling and variational inference.
