: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models.
: Tools like ArviZ allow you to plot posterior distributions or trace plots to check for convergence. pymc regression tutorial
: 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 : By default, PyMC uses the No-U-Turn Sampler
: You assign probability distributions to unknown parameters like the intercept ( ), slope ( ), and error ( ). Common choices include: pm.Normal for regression coefficients. pm.HalfNormal or pm.HalfCauchy for the standard deviation ( ) to ensure it remains positive. For linear regression
: This is the core formula, typically defined as mu = intercept + slope * x .
: This connects the model to your observed data. For linear regression, the outcome variable is usually modeled as a Normal distribution: pm.Normal("y", mu=mu, sigma=sigma, observed=y) . 2. Inference and Sampling
After sampling, you analyze the results to understand parameter uncertainty.