Build and analyze Bayesian models with PyMC for MCMC and variational inference.
npx skills add https://github.com/galeep/plugin-place --skill pymc-galeep
Please help me install this Agent Skill. Skill: pymc Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-machine-learning/skills/pymc Command: npx skills add https://github.com/galeep/plugin-place --skill pymc-galeep
This Skill provides a comprehensive toolkit for building and analyzing Bayesian models using PyMC, a Python library for probabilistic programming and inference.
Use the pymc skill to fit a linear regression model to your data and extract the posterior distributions for the coefficients.
High-intent search queries and answers about installing and using this skill.
Build Bayesian models for time series data using PyMC by defining probabilistic relationships, fitting trends and seasonality, and extracting posterior distributions for model validation and inference.
Compare Bayesian models using PyMC by calculating Leave-One-Out (LOO) and Watanabe-Akaike Information Criterion (WAIC) metrics to evaluate predictive accuracy and select the most appropriate statistical model.
Diagnose MCMC convergence and divergence issues by checking effective sample size and convergence metrics using PyMC alongside ArviZ, ensuring reliable posterior distributions for your Bayesian inference.
Perform variational inference as an alternative to MCMC sampling in PyMC to estimate posterior distributions faster, providing scalable probabilistic modeling for large datasets and complex models.
PyMC integrates directly with pandas and numpy, allowing you to process input arrays and DataFrames to build and analyze hierarchical Bayesian models and extract posterior coefficients efficiently.
Clay-HHKScale pandas and NumPy workloads across cores or clusters with lazy evaluation.
fuzzy-dynamicsBuild and infer Bayesian models with MCMC sampling and variational inference.
yf8578Build and validate Bayesian models with PyMC and ArviZ.
LeonChaoXBuild Bayesian models in PyMC with MCMC sampling and posterior inference.
