Bayesian inference with INLA (Paperback, 1)

Introduction to Bayesian Inference Introduction Bayesian inference Conjugate priors Computational methods Markov chain Monte Carlo The integrated nested Laplace approximation An introductory example: U’s in Game of Thrones books Final remarks The Integrated Nested Laplace Approximation Introduction The Integrated Nested Laplace Approximation The R-INLA package Model assessment and model choice Control options Working with posterior marginals Sampling from the posterior Mixed-effects Models Introduction Fixed-effects models Types of mixed-effects models Information on the latent effects Additional arguments Final remarks Multilevel Models Introduction Multilevel models with random effects Multilevel models with nested effects Multilevel models with complex structure Multilevel models for longitudinal data Multilevel models for binary data Multilevel models for count data Priors in R-INLA Introduction Selection of priors Implementing new priors Penalized Complexity priors Sensitivity analysis with R-INLA Scaling effects and priors Final remarks Advanced Features Introduction Predictor Matrix Linear combinations Several likelihoods Shared terms Linear constraints Final remarks Spatial Models Introduction Areal data Geostatistics Point patterns Temporal Models Introduction Autoregressive models Non-Gaussian data Forecasting Space-state models Spatio-temporal models Final remarks Smoothing Introduction Splines Smooth terms with INLA Smoothing with SPDE Non-Gaussian models Final remarks Survival Models Introduction Non-parametric estimation of the survival curve Parametric modeling of the survival function Semi-parametric estimation: Cox proportional hazards Accelerated failure time models Frailty models Joint modeling Implementing New Latent Models Introduction Spatial latent effects R implementation with rgeneric Bayesian model averaging INLA within MCMC Comparison of results Final remarks Missing Values and Imputation Introduction Missingness mechanism Missing values in the response Imputation of missing covariates Multiple imputation of missing values Final remarks 13.Mixture models Introduction Bayesian analysis of mixture models Fitting mixture models with INLA Model selection for mixture models Cure rate models Final remarks Packages used in the book