JAGS uses a Markov Chain Monte Carlo (MCMC) method to generate samples from the posterior distribution of model parameters. This is particularly useful in Bayesian inference, where the goal is to update the probability of a hypothesis as more evidence or information becomes available. The software allows for flexible model specification, making it an ideal tool for epidemiologists working with complex models.
