# New frontiers in Bayesian modeling using the INLA package in R

arXiv: https://arxiv.org/abs/1907.10426 · HTML: https://mlsanity.com/paper/1907.10426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Multiresolution kernel matrix algebra](https://mlsanity.com/paper/2211.11681/) (2022)
- [Low-rank variational Bayes correction to the Laplace method](https://mlsanity.com/paper/2111.12945/) (2021)

## Abstract (arXiv)

The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It is a non-sampling based framework which provides approximate results for Bayesian inference, using sparse matrices. The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era. In this paper, we present new developments within the INLA package with the aim to provide a computationally efficient mechanism for the Bayesian inference of relevant challenging situations.