Linear regression in which coefficients and error variance are treated as random variables with prior distributions, yielding full posterior distributions over parameters and predictions rather than point estimates. With conjugate Normal-Inverse-Gamma priors the posterior is available in closed form; otherwise it is obtained via MCMC or variational inference.