Integrated nested Laplace approximations

Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's approximation. ^[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 ${\displaystyle {\boldsymbol {\theta }}}$: $${\displaystyle \pi ({\boldsymbol {y}}|{\boldsymbol {x}},{\boldsymbol {\theta }})=\prod _{i\in {\mathcal {I}}}\pi (y_{i}|\eta _{i},{\boldsymbol {\theta }}),}$$ 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 $${\displaystyle \pi ({\boldsymbol {x}}|{\boldsymbol {\theta }})\propto \left|{\boldsymbol {Q_{\theta }}}\right|^{1/2}\exp \left(-{\frac {1}{2}}{\boldsymbol {x}}^{T}{\boldsymbol {Q_{\theta }}}{\boldsymbol {x}}\right),}$$ 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 $${\displaystyle \pi ({\boldsymbol {x}},{\boldsymbol {\theta }}|{\boldsymbol {y}})={\frac {\pi ({\boldsymbol {y}}|{\boldsymbol {x}},{\boldsymbol {\theta }})\pi ({\boldsymbol {x}}|{\boldsymbol {\theta }})\pi ({\boldsymbol {\theta }})}{\pi ({\boldsymbol {y}})}},}$$ the joint posterior distribution of ${\displaystyle {\boldsymbol {x}}}$ and ${\displaystyle {\boldsymbol {\theta }}}$ is given by $${\displaystyle {\begin{aligned}\pi ({\boldsymbol {x}},{\boldsymbol {\theta }}|{\boldsymbol {y}})&\propto \pi ({\boldsymbol {\theta }})\pi ({\boldsymbol {x}}|{\boldsymbol {\theta }})\prod _{i}\pi (y_{i}|\eta _{i},{\boldsymbol {\theta }})\\&\propto \pi ({\boldsymbol {\theta }})\left|{\boldsymbol {Q_{\theta }}}\right|^{1/2}\exp \left(-{\frac {1}{2}}{\boldsymbol {x}}^{T}{\boldsymbol {Q_{\theta }}}{\boldsymbol {x}}+\sum _{i}\log \left[\pi (y_{i}|\eta _{i},{\boldsymbol {\theta }})\right]\right).\end{aligned}}}$$ Obtaining the exact posterior is generally a very difficult problem. In INLA, the main aim is to approximate the posterior marginals $${\displaystyle {\begin{array}{rcl}\pi (x_{i}|{\boldsymbol {y}})&=&\int \pi (x_{i}|{\boldsymbol {\theta }},{\boldsymbol {y}})\pi ({\boldsymbol {\theta }}|{\boldsymbol {y}})d{\boldsymbol {\theta }}\\\pi (\theta _{j}|{\boldsymbol {y}})&=&\int \pi ({\boldsymbol {\theta }}|{\boldsymbol {y}})d{\boldsymbol {\theta }}_{-j},\end{array}}}$$ 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 $${\displaystyle {\begin{array}{rcl}{\widetilde {\pi }}(x_{i}|{\boldsymbol {y}})&=&\int {\widetilde {\pi }}(x_{i}|{\boldsymbol {\theta }},{\boldsymbol {y}}){\widetilde {\pi }}({\boldsymbol {\theta }}|{\boldsymbol {y}})d{\boldsymbol {\theta }}\\{\widetilde {\pi }}(\theta _{j}|{\boldsymbol {y}})&=&\int {\widetilde {\pi }}({\boldsymbol {\theta }}|{\boldsymbol {y}})d{\boldsymbol {\theta }}_{-j},\end{array}}}$$ 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 $${\displaystyle {\begin{aligned}{\widetilde {\pi }}(x_{i}|{\boldsymbol {y}})=\sum _{k}{\widetilde {\pi }}\left(x_{i}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)\times {\widetilde {\pi }}({\boldsymbol {\theta }}_{k}|{\boldsymbol {y}})\times \Delta _{k},\end{aligned}}}$$ 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 $${\displaystyle {\begin{aligned}{\pi }({\boldsymbol {\theta }}|{\boldsymbol {y}})={\frac {\pi \left({\boldsymbol {x}},{\boldsymbol {\theta }},{\boldsymbol {y}}\right)}{\pi \left({\boldsymbol {x}}|{\boldsymbol {\theta }},{\boldsymbol {y}}\right)\pi ({\boldsymbol {y}})}},\end{aligned}}}$$ 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] $${\displaystyle {\begin{aligned}{\widetilde {\pi }}({\boldsymbol {\theta }}_{k}|{\boldsymbol {y}})&\propto \left.{\frac {\pi \left({\boldsymbol {x}},{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}{{\widetilde {\pi }}_{G}\left({\boldsymbol {x}}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}}\right\vert _{{\boldsymbol {x}}={\boldsymbol {x}}^{*}({\boldsymbol {\theta }}_{k})},\\&\propto \left.{\frac {\pi ({\boldsymbol {y}}|{\boldsymbol {x}},{\boldsymbol {\theta }}_{k})\pi ({\boldsymbol {x}}|{\boldsymbol {\theta }}_{k})\pi ({\boldsymbol {\theta }}_{k})}{{\widetilde {\pi }}_{G}\left({\boldsymbol {x}}|{\boldsymbol {\theta }}_{k},{\boldsymbol {y}}\right)}}\right\vert _{{\boldsymbol {x}}={\boldsymbol {x}}^{*}({\boldsymbol {\theta }}_{k})},\end{aligned}}}$$ 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

1. Rue, Håvard; Martino, Sara; Chopin, Nicolas (2009). "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations". J. R. Statist. Soc. B. 71 (2): 319–392. doi:10.1111/j.1467-9868.2008.00700.x. hdl: 2066/75507 . S2CID   1657669.
2. Taylor, Benjamin M.; Diggle, Peter J. (2014). "INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes". Journal of Statistical Computation and Simulation. 84 (10): 2266–2284. arXiv: 1202.1738 . doi:10.1080/00949655.2013.788653. S2CID   88511801.
3. Teng, M.; Nathoo, F.; Johnson, T. D. (2017). "Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods". Journal of Statistical Computation and Simulation. 87 (11): 2227–2252. doi:10.1080/00949655.2017.1326117. PMC   5708893 . PMID   29200537.
4. Wang, Xiaofeng; Yue, Yu Ryan; Faraway, Julian J. (2018). Bayesian Regression Modeling with INLA. Chapman and Hall/CRC. ISBN   9781498727259.
5. Blangiardo, Marta; Cameletti, Michela (2015). Spatial and Spatio-temporal Bayesian Models with R-INLA. John Wiley & Sons, Ltd. ISBN   9781118326558.
6. Opitz, T. (2017). "Latent Gaussian modeling and INLA: A review with focus on space-time applications". Journal de la Société Française de Statistique. 158: 62–85. arXiv: 1708.02723 .
7. Moraga, Paula (2019). Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapman and Hall/CRC. ISBN   9780367357955.
8. Lindgren, Finn; Rue, Håvard; Lindström, Johan (2011). "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach". J. R. Statist. Soc. B. 73 (4): 423–498. doi:10.1111/j.1467-9868.2011.00777.x. hdl: 20.500.11820/1084d335-e5b4-4867-9245-ec9c4f6f4645 . S2CID   120949984.
9. Lezama-Ochoa, N.; Grazia Pennino, M.; Hall, M. A.; Lopez, J.; Murua, H. (2020). "Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)". Scientific Reports. 10 (1): 18822. Bibcode:2020NatSR..1018822L. doi:10.1038/s41598-020-73879-3. PMC   7606447 . PMID   33139744.
10. "R-INLA Project" . Retrieved 21 April 2022.

Further reading

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