: The sampling process produces a Trace (often stored in an InferenceData object via ArviZ), which contains the posterior samples for every parameter. 3. Posterior Analysis
: Unlike frequentist confidence intervals, Bayesian credible intervals (e.g., a 94% HDI) provide a direct probability that a parameter falls within a certain range. 4. Advanced Regression Types pymc regression tutorial
After sampling, you analyze the results to understand parameter uncertainty. : The sampling process produces a Trace (often
: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models. Bayesian credible intervals (e.g.
Once the model is specified, you run the "Inference Button" by calling pm.sample() .
PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation