INLA

Uses

How it works

Comparisons

Highlighted claims

• The paper proposes using INLA to address computational and software barriers in flexible Bayesian joint models. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The model is formulated as a latent Gaussian model so that INLA can approximate posterior marginals deterministically. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The final model treats scaling weights evaluated from preliminary posterior expectations as deterministic while keeping the latent process jointly estimated. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• Compared with MCMC-based flexible joint models, INLA was faster and avoided sampling convergence problems. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• An internal calibration step keeps the non-linear association compatible with INLA's latent Gaussian structure. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA