What are some challenges associated with using rjags?
While rjags is a powerful tool, there are some challenges associated with its use:
Computational intensity: MCMC simulations can be computationally intensive, requiring substantial time and resources for complex models. Convergence issues: Ensuring that the MCMC chains have converged to the target distribution can be difficult and requires careful diagnostic checks. Model specification: Defining the appropriate model structure and priors can be challenging, especially for complex epidemiological data.
