Integrated nested Laplace approximations

Integrated Nested Laplace Approximations (INLA) is a computational method used for Bayesian inference, particularly in the context of latent Gaussian models. It provides a way to perform approximate Bayesian inference that is often more efficient and faster than traditional Markov Chain Monte Carlo (MCMC) methods. INLA has gained popularity due to its applicability in a wide range of statistical models, especially in fields such as spatial statistics, ecology, and epidemiology.