A Bayesian model consists of three main components: the prior distribution, the likelihood function, and the posterior distribution. The prior distribution represents the initial beliefs about the parameters before observing the data. The likelihood function is the probability of the observed data given the parameters. The posterior distribution, which is the goal of Bayesian inference, combines the prior distribution and the likelihood function to update our beliefs after observing the data.
