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Found 5 Skills
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Use when "PyMC", "Bayesian", "MCMC", "probabilistic programming", or asking about "Bayesian regression", "hierarchical model", "NUTS sampler", "posterior distribution", "prior predictive", "credible intervals", "uncertainty quantification"
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
Build, revise, and debug Bayesian models with PyMC. Use when choosing likelihoods and priors, specifying model/data dimensions, fitting hierarchical models, GPs, time series, mixtures, BART or splines, selecting inference methods, resolving failed initial evaluation or model/data shape errors, generating predictions, or testing model code with simulated data and simulation-based calibration (SBC). Covers PyMC 6+, PyTensor 3+ and ArviZ 1.0 DataTree workflows.