Tag Archives: Stan

For most Bayesian model fittings, there is no analytical solution for deriving posterior distributions or integrating them. Rather, we approximate those posteriors via random sampling from all possible parameter sets and their corresponding likelihood functions. Specifically, we sample posterior distributions from a multidimensional space, where each dimension corresponds to a parameter. The shape of thisContinue reading “A simple Bayesian regression model with Stan: brms”