Abstract
We explore the logist regression model, estimating its parameters through maximum likelihood and Bayesian estimators.
We use mixtures of distributions for 𝐾 = 1 and 2 components.
The Hamiltonian Monte Carlo implemented in Stan is used to obtain the Bayesian estimates and R for the MLEs.