# American Institute of Mathematical Sciences

March  2019, 1(1): 39-60. doi: 10.3934/fods.2019002

## Approximate Bayesian inference for geostatistical generalised linear models

 Department of Mathematical Sciences, University of Bath, BA2 7AY, Bath, UK

* Corresponding author: Evangelos Evangelou

Published  March 2019

The aim of this paper is to bring together recent developments in Bayesian generalised linear mixed models and geostatistics. We focus on approximate methods on both areas. A technique known as full-scale approximation, proposed by Sang and Huang (2012) for improving the computational drawbacks of large geostatistical data, is incorporated into the INLA methodology, used for approximate Bayesian inference. We also discuss how INLA can be used for approximating the posterior distribution of transformations of parameters, useful for practical applications. Issues regarding the choice of the parameters of the approximation such as the knots and taper range are also addressed. Emphasis is given in applications in the context of disease mapping by illustrating the methodology for modelling the loa loa prevalence in Cameroon and malaria in the Gambia.

Citation: Evangelos Evangelou. Approximate Bayesian inference for geostatistical generalised linear models. Foundations of Data Science, 2019, 1 (1) : 39-60. doi: 10.3934/fods.2019002
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Scaled Frobenius norm ($\circ$) and computational time ($+$) against taper range $\gamma$
Locations for the simulated example, indicated by $\cdot$, and grid for the full scale approximation, indicated by $\times$. Prediction is considered at a central site ($\circ$) and a far site ($\square$)
Posterior densities for (a) logarithm of sill, (b) range, and (c) intercept. The histogram shows the MCMC sample. The approximation using INLA and exact covariance matrix is shown by a solid line, the INLA with the full scale approximation is shown by a dashed line, and the INLA with the predictive process approximation is shown by a dotted line. The true parameter value is indicated by a triangle on the horizontal axis
Predictive distribution of the random field at a central site (left) and a far site (right). The histogram shows the MCMC sample. The approximation using INLA and exact covariance matrix is shown by a solid line, the INLA with the full scale approximation is shown by a dashed line, and the INLA with the predictive process approximation is shown by a dotted line
Predicted prevalence of the loa loa parasite (top), and prediction standard deviation (bottom)
Posterior plots for the variance parameters. (a) Joint posterior of $\log(\sigma^2)$ and $\rho$ using exact INLA, (b) Marginal posterior of $\log(\sigma^2)$, (c) Marginal posterior of $\rho$. The histogram is for the MCMC sample, the exact INLA is shown by a solid line and the full-scale INLA by a dadhed line
Posterior for the regressor coefficients. The histogram is for the MCMC sample, the exact INLA is shown by a solid line and the full-scale INLA by a dashed line
Sampled locations for the Gambia data from [30]
Posterior densities for the parameters (a) $\tau^2$, (b) $\sigma^2$, and (c) $\rho$ of the Gambia malaria data
Prediction of spatial random field for the Gambia malaria data (top) and prediction standard deviation (bottom)
Parameter estimates for the loa loa prevalence in Cameroon using exact INLA, approximate INLA, and MCMC
 Parameter Exact INLA Estimate 95% interval Intercept $(\beta_0)$ $-14.17$ $-18.58$ $-9.76$ Elevation $0-.65$Km $(\beta_1)$ $2.28$ $1.07$ $3.49$ Elevation $.65 - 1$Km $(\beta_2)$ $1.62$ $0.90$ $2.34$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.81$ $0.17$ $1.45$ Max(NDVI) $(\beta_4)$ $14.09$ $8.00$ $20.17$ Sd(NDVI) $(\beta_5)$ $0.71$ $-9.68$ $11.10$ Sill $(\sigma^2)$ $0.72$ $0.50$ $1.02$ Range $(\rho)$ $0.55$ $0.25$ $1.08$ Parameter Full-scale INLA Estimate 95% interval Intercept $(\beta_0)$ $-15.03$ $-19.28$ $-10.77$ Elevation $0-.65$Km $(\beta_1)$ $2.19$ $1.02$ $3.36$ Elevation $.65 - 1$Km $(\beta_2)$ $1.60$ $0.91$ $2.29$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.68$ $0.05$ $1.30$ Max(NDVI) $(\beta_4)$ $15.11$ $9.16$ $21.06$ Sd(NDVI) $(\beta_5)$ $1.27$ $-8.87$ $11.42$ Sill $(\sigma^2)$ $0.66$ $0.45$ $0.94$ Range $(\rho)$ $0.64$ $0.36$ $1.08$ Parameter MCMC Estimate 95% interval Intercept $(\beta_0)$ $-14.67$ $-19.16$ $-9.90$ Elevation $0-.65$Km $(\beta_1)$ $2.35$ $1.15$ $3.60$ Elevation $.65 - 1$Km $(\beta_2)$ $1.68$ $1.00$ $2.39$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.83$ $0.18$ $1.46$ Max(NDVI) $(\beta_4)$ $14.66$ $8.19$ $20.82$ Sd(NDVI) $(\beta_5)$ $0.68$ $-9.04$ $11.22$ Sill $(\sigma^2)$ $0.70$ $0.51$ $0.99$ Range $(\rho)$ $0.48$ $0.28$ $0.92$
 Parameter Exact INLA Estimate 95% interval Intercept $(\beta_0)$ $-14.17$ $-18.58$ $-9.76$ Elevation $0-.65$Km $(\beta_1)$ $2.28$ $1.07$ $3.49$ Elevation $.65 - 1$Km $(\beta_2)$ $1.62$ $0.90$ $2.34$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.81$ $0.17$ $1.45$ Max(NDVI) $(\beta_4)$ $14.09$ $8.00$ $20.17$ Sd(NDVI) $(\beta_5)$ $0.71$ $-9.68$ $11.10$ Sill $(\sigma^2)$ $0.72$ $0.50$ $1.02$ Range $(\rho)$ $0.55$ $0.25$ $1.08$ Parameter Full-scale INLA Estimate 95% interval Intercept $(\beta_0)$ $-15.03$ $-19.28$ $-10.77$ Elevation $0-.65$Km $(\beta_1)$ $2.19$ $1.02$ $3.36$ Elevation $.65 - 1$Km $(\beta_2)$ $1.60$ $0.91$ $2.29$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.68$ $0.05$ $1.30$ Max(NDVI) $(\beta_4)$ $15.11$ $9.16$ $21.06$ Sd(NDVI) $(\beta_5)$ $1.27$ $-8.87$ $11.42$ Sill $(\sigma^2)$ $0.66$ $0.45$ $0.94$ Range $(\rho)$ $0.64$ $0.36$ $1.08$ Parameter MCMC Estimate 95% interval Intercept $(\beta_0)$ $-14.67$ $-19.16$ $-9.90$ Elevation $0-.65$Km $(\beta_1)$ $2.35$ $1.15$ $3.60$ Elevation $.65 - 1$Km $(\beta_2)$ $1.68$ $1.00$ $2.39$ Elevation $1 - 1.3$Km $(\beta_3)$ $0.83$ $0.18$ $1.46$ Max(NDVI) $(\beta_4)$ $14.66$ $8.19$ $20.82$ Sd(NDVI) $(\beta_5)$ $0.68$ $-9.04$ $11.22$ Sill $(\sigma^2)$ $0.70$ $0.51$ $0.99$ Range $(\rho)$ $0.48$ $0.28$ $0.92$
Parameter estimates of the Gambia malaria data
 Parameter Estimate 95% interval Intercept ($\beta_0$) $-0.07309$ $-2.95100$ $2.80483$ Age ($\beta_1$) $0.00066$ $0.00042$ $0.00090$ Untreated bed net ($\beta_2$) $-0.36216$ $-0.67639$ $-0.04793$ Treated bed net ($\beta_3$) $-0.68297$ $-1.07497$ $-0.29097$ Greenness ($\beta_4$) $-0.01334$ $-0.07507$ $0.04839$ PHC ($\beta_5$) $-0.32790$ $-0.77921$ $0.12340$ Area 2 ($\beta_6$) $-0.69385$ $-2.26728$ $0.87958$ Area 3 ($\beta_7$) $-0.78240$ $-2.44258$ $0.87778$ Area 4 ($\beta_8$) $0.65537$ $-1.12152$ $2.43226$ Area 5 ($\beta_9$) $0.97627$ $-0.80963$ $2.76217$ Nugget ($\tau^2$) $0.13209$ $0.00310$ $0.26136$ Sill ($\sigma^2$) $0.98459$ $0.34501$ $1.82461$ Range ($\rho$) $9.82025$ $0.54713$ $18.63800$
 Parameter Estimate 95% interval Intercept ($\beta_0$) $-0.07309$ $-2.95100$ $2.80483$ Age ($\beta_1$) $0.00066$ $0.00042$ $0.00090$ Untreated bed net ($\beta_2$) $-0.36216$ $-0.67639$ $-0.04793$ Treated bed net ($\beta_3$) $-0.68297$ $-1.07497$ $-0.29097$ Greenness ($\beta_4$) $-0.01334$ $-0.07507$ $0.04839$ PHC ($\beta_5$) $-0.32790$ $-0.77921$ $0.12340$ Area 2 ($\beta_6$) $-0.69385$ $-2.26728$ $0.87958$ Area 3 ($\beta_7$) $-0.78240$ $-2.44258$ $0.87778$ Area 4 ($\beta_8$) $0.65537$ $-1.12152$ $2.43226$ Area 5 ($\beta_9$) $0.97627$ $-0.80963$ $2.76217$ Nugget ($\tau^2$) $0.13209$ $0.00310$ $0.26136$ Sill ($\sigma^2$) $0.98459$ $0.34501$ $1.82461$ Range ($\rho$) $9.82025$ $0.54713$ $18.63800$
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