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Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression , spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical(More)
Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalised) linear models, (generalised) additive models , smoothing-spline models, state-space models, semiparametric regression, spatial and spatio-temporal models, log-Gaussian Cox-processes, and(More)
Many commonly used models in statistics can be formulated as (Bayesian) hierarchical Gaus-sian Markov random field models. These are characterised by assuming a (often large) Gaussian Markov random field (GMRF) as the second stage in the hierarchical structure and a few hyper-parameters at the third stage. Markov chain Monte Carlo is the common approach for(More)
This manual describes the inla program, a new instrument which allows the user to easily perform approximate Bayesian inference using integrated nested Laplace approximation (INLA). We describe the set of models which can be solved by the inla program and provide a series of worked out examples illustrating its usage in details. Appendix A contains a(More)
Many commonly used models in statistics can be formulated as Hierarchical Gaussian Markov random field (GMRF) models. These are characterised by assuming a (often large) GMRF as the second stage in the hierarchical model and a few hyperparameters at the third stage. Markov chain Monte Carlo is the common approach to do inference from such models. The(More)
Volatility in financial time series is mainly analysed through two classes of models ; the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models and the Stochastic Volatility (SV) ones. GARCH models are straightforward to estimate using maximum likelihood techniques, while SV models require more complex inferential and computational(More)
Bayesian analysis of time-to-event data, usually called survival analysis, has received increasing attention in the last years. In Cox-type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated. In general, Bayesian methods(More)
Animal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast, nonsampling-based Bayesian inference for hierarchical Gaussian Markov models. In this article, we demonstrate that the INLA methodology can(More)
This manual describes the inla program, a new instrument which allows the user to easily perform approximate Bayesian inference using integrated nested Laplace approximation (INLA). We describe the set of models which can be solved by the inla program and provide a series of worked out examples illustrating its usage in details. Appendix A contains a(More)