Tutorial | Pymc Regression

Once the model is specified, you run the "Inference Button" by calling pm.sample() .

After sampling, you analyze the results to understand parameter uncertainty. pymc regression tutorial

: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models. Once the model is specified, you run the

: 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. : You assign probability distributions to unknown parameters

: 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

: 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