Integrated Nested Laplace Approximations (INLA) is a computational method used for Bayesian inference, specifically designed to analyze latent Gaussian models. This technique simplifies the process of obtaining posterior distributions, making it an efficient alternative to traditional Markov Chain Monte Carlo (MCMC) methods. INLA is particularly useful in scenarios involving complex models where computational resources may be limited.
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