There are several reasons why JAGS is beneficial for epidemiological research:
Flexibility: JAGS allows for the construction of complex models that can easily incorporate various factors such as covariates, interactions, and non-linear relationships. Bayesian Inference: The Bayesian approach provides a natural way to incorporate prior knowledge and deal with uncertainty, which is often a crucial aspect in epidemiological studies. MCMC: The MCMC methods used by JAGS are powerful for sampling from complex posterior distributions, which are often encountered in epidemiological models.
