arXiv:2605.20345v1 Announce Type: cross Abstract: 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 fro
This paper presents a refinement to a popular approximate Bayesian inference method, indicating ongoing research and improvements in core AI/ML techniques.
Improved inference methods for Latent Gaussian Models can lead to more accurate and efficient Bayesian statistical applications, impacting fields reliant on complex probabilistic modeling.
The proposed 'Corrected Integrated Laplace Approximation' offers a potentially more reliable approximation for Bayesian inference in certain models, reducing the divergence from exact posteriors.
- · AI/ML researchers and practitioners
- · Bioinformatics and healthcare modeling
- · Spatial statistics and environmental science
- · Financial modeling
- · Users of less accurate approximate inference methods
- · Computational systems with limited resources running less efficient algorithms
More robust and reliable application of Latent Gaussian Models in various scientific and engineering disciplines.
Accelerated development cycles for new models and applications due to improved inference efficiency and accuracy.
Potentially broader adoption of Bayesian methods in industries that previously found them too computationally intensive or unreliable for complex, non-Gaussian likelihoods.
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Read at arXiv cs.LG