Integrated nested Laplace approximations

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Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method.^[1] It is designed for a class of models called latent Gaussian models (LGMs), for which it can be a fast and accurate alternative for Markov chain Monte Carlo methods to compute posterior marginal distributions.^[2][3][4] Due to its relative speed even with large data sets for certain problems and models, INLA has been a popular inference method in applied statistics, in particular spatial statistics, ecology, and epidemiology.^[5][6][7] It is also possible to combine INLA with a finite element method solution of a stochastic partial differential equation to study e.g. spatial point processes and species distribution models.^[8][9] The INLA method is implemented in the R-INLA R package.^[10]

Latent Gaussian models

Let ${\displaystyle {\boldsymbol {y}}=(y_{1},\dots ,y_{n})}$ denote the response variable (that is, the observations) which belongs to an exponential family, with the mean ${\displaystyle \mu _{i}}$ (of ${\displaystyle y_{i}}$) being linked to a linear predictor ${\displaystyle \eta _{i}}$ via an appropriate link function. The linear predictor can take the form of a (Bayesian) additive model. All latent effects (the linear predictor, the intercept, coefficients of possible covariates, and so on) are collectively denoted by the vector ${\displaystyle {\boldsymbol {x}}}$. The hyperparameters of the model are denoted by ${\displaystyle {\boldsymbol {\theta }}}$. As per Bayesian statistics, ${\displaystyle {\boldsymbol {x}}}$ and ${\displaystyle {\boldsymbol {\theta }}}$ are random variables with prior distributions.

The observations are assumed to be conditionally independent given ${\displaystyle {\boldsymbol {x}}}$ and ${\displaystyle {\boldsymbol {\theta }}}$:

where ${\displaystyle {\mathcal {I}}}$ is the set of indices for observed elements of ${\displaystyle {\boldsymbol {y}}}$ (some elements may be unobserved, and for these INLA computes a posterior predictive distribution). Note that the linear predictor ${\displaystyle {\boldsymbol {\eta }}}$ is part of ${\displaystyle {\boldsymbol {x}}}$.

For the model to be a latent Gaussian model, it is assumed that ${\displaystyle {\boldsymbol {x}}|{\boldsymbol {\theta }}}$ is a Gaussian Markov Random Field (GMRF)^[1] (that is, a multivariate Gaussian with additional conditional independence properties) with probability density

where ${\displaystyle {\boldsymbol {Q_{\theta }}}}$ is a ${\displaystyle {\boldsymbol {\theta }}}$-dependent sparse precision matrix and ${\displaystyle \left|{\boldsymbol {Q_{\theta }}}\right|}$ is its determinant. The precision matrix is sparse due to the GMRF assumption. The prior distribution ${\displaystyle \pi ({\boldsymbol {\theta }})}$ for the hyperparameters need not be Gaussian. However, the number of hyperparameters, ${\displaystyle m=\mathrm {dim} ({\boldsymbol {\theta }})}$, is assumed to be small (say, less than 15).

Approximate Bayesian inference with INLA

In Bayesian inference, one wants to solve for the posterior distribution of the latent variables ${\displaystyle {\boldsymbol {x}}}$ and ${\displaystyle {\boldsymbol {\theta }}}$. Applying Bayes' theorem

the joint posterior distribution of ${\displaystyle {\boldsymbol {x}}}$ and ${\displaystyle {\boldsymbol {\theta }}}$ is given by Obtaining the exact posterior is generally a very difficult problem. In INLA, the main aim is to approximate the posterior marginals where ${\displaystyle {\boldsymbol {\theta }}_{-j}=\left(\theta _{1},\dots ,\theta _{j-1},\theta _{j+1},\dots ,\theta _{m}\right)}$.

A key idea of INLA is to construct nested approximations given by

where ${\displaystyle {\widetilde {\pi }}(\cdot |\cdot )}$ is an approximated posterior density. The approximation to the marginal density ${\displaystyle \pi (x_{i}|{\boldsymbol {y}})}$ is obtained in a nested fashion by first approximating ${\displaystyle \pi ({\boldsymbol {\theta }}|{\boldsymbol {y}})}$ and ${\displaystyle \pi (x_{i}|{\boldsymbol {\theta }},{\boldsymbol {y}})}$, and then numerically integrating out ${\displaystyle {\boldsymbol {\theta }}}$ as where the summation is over the values of ${\displaystyle {\boldsymbol {\theta }}}$, with integration weights given by ${\displaystyle \Delta _{k}}$. The approximation of ${\displaystyle \pi (\theta _{j}|{\boldsymbol {y}})}$ is computed by numerically integrating ${\displaystyle {\boldsymbol {\theta }}_{-j}}$ out from ${\displaystyle {\widetilde {\pi }}({\boldsymbol {\theta }}|{\boldsymbol {y}})}$.

To get the approximate distribution ${\displaystyle {\widetilde {\pi }}({\boldsymbol {\theta }}|{\boldsymbol {y}})}$, one can use the relation

as the starting point. Then ${\displaystyle {\widetilde {\pi }}({\boldsymbol {\theta }}|{\boldsymbol {y}})}$ is obtained at a specific value of the hyperparameters ${\displaystyle {\boldsymbol {\theta }}={\boldsymbol {\theta }}_{k}}$ with Laplace's approximation^[1] where ${\displaystyle {\widetilde {\pi }}_{G}\left({\boldsymbol {x}}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}$ is the  Gaussian approximation to ${\displaystyle {\pi }\left({\boldsymbol {x}}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}$ whose mode at a given ${\displaystyle {\boldsymbol {\theta }}_{k}}$ is ${\displaystyle {\boldsymbol {x}}^{*}({\boldsymbol {\theta }}_{k})}$. The mode can be found numerically for example with the Newton-Raphson method.

The trick in the Laplace approximation above is the fact that the Gaussian approximation is applied on the full conditional of ${\displaystyle {\boldsymbol {x}}}$ in the denominator since it is usually close to a Gaussian due to the GMRF property of ${\displaystyle {\boldsymbol {x}}}$. Applying the approximation here improves the accuracy of the method, since the posterior ${\displaystyle {\pi }({\boldsymbol {\theta }}|{\boldsymbol {y}})}$ itself need not be close to a Gaussian, and so the Gaussian approximation is not directly applied on ${\displaystyle {\pi }({\boldsymbol {\theta }}|{\boldsymbol {y}})}$. The second important property of a GMRF, the sparsity of the precision matrix ${\displaystyle {\boldsymbol {Q}}_{{\boldsymbol {\theta }}_{k}}}$, is required for efficient computation of ${\displaystyle {\widetilde {\pi }}({\boldsymbol {\theta }}_{k}|{\boldsymbol {y}})}$ for each value ${\displaystyle {{\boldsymbol {\theta }}_{k}}}$.^[1]

Obtaining the approximate distribution ${\displaystyle {\widetilde {\pi }}\left(x_{i}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}$ is more involved, and the INLA method provides three options for this: Gaussian approximation, Laplace approximation, or the simplified Laplace approximation.^[1] For the numerical integration to obtain ${\displaystyle {\widetilde {\pi }}(x_{i}|{\boldsymbol {y}})}$, also three options are available: grid search, central composite design, or empirical Bayes.^[1]

References

Further reading

• Gomez-Rubio, Virgilio (2021). Bayesian inference with INLA. Chapman and Hall/CRC. ISBN 978-1-03-217453-2.