Pymc Regression Tutorial < NEWEST ✧ >

: 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

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

: 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