Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Efficient Bayesian inference of LGMs often requires marginalizing out the latent variables. For LGMs with a non-Gaussian likelihood, exact marginalization is not possible and a popular approach is to do approximate marginalization with an integrated Laplace approximation (ILA). Using ILA produces an approximate posterior which, in some settings, can differ significantly from the correct posterior, which impacts downstream applications. We propose an importance sampling scheme to correct the error introduced by ILA. By...
| Authors | Jinlin Lai, Charles C. Margossian, Daniel R. Sheldon |
|---|---|
| Primary category | stat.ML |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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