This course discusses computational techniques for statistical inference, including exact recursions for hidden Markov chains, Gaussian Markov random fields and their applications in latent Gaussian models, inference for latent Gaussian models using Markov chain Monte Carlo with block-sampling and auxiliary variables, deterministic approximations using integrated nested Laplace approximations (INLA), and the EM algorithm.