: This is the core formula, typically defined as mu = intercept + slope * x .
: By default, PyMC uses the No-U-Turn Sampler (NUTS) , an efficient algorithm for complex Bayesian models. pymc regression tutorial
PyMC supports more complex regression structures beyond simple linear models: GLM: Linear regression — PyMC dev documentation : This is the core formula, typically defined
PyMC provides a flexible framework for Bayesian linear regression, allowing you to model data by defining prior knowledge and likelihood functions. Unlike frequentist approaches that find a single "best" set of coefficients, PyMC generates a distribution of possible parameters (the posterior) using Markov Chain Monte Carlo (MCMC) sampling. 1. Model Definition : This is the core formula