2021/01/12 by Alex Stringer, Stringer, Alex · 2 citations
Computer Science · Environmental Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Soil Geostatistics and Mapping #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2101.04468
openalex publication_date 2021/01/12 · openalex created_date 2021/01/18 · openalex updated_date 2026/07/28
The aghq package for implementing approximate Bayesian inference using adaptive quadrature is introduced. The method and software are described, and use of the package in making approximate Bayesian inferences in several challenging low- and high-dimensional models is illustrated. Examples include an infectious disease model; an astrostatistical model for estimating the mass of the Milky Way; two examples in non-Gaussian model-based geostatistics including one incorporating zero-inflation which is not easily fit using other methods; and a model for zero-inflated, overdispersed count data. The aghq package is especially compatible with the popular TMB interface for automatic differentiation and Laplace approximation, and existing users of that software can make approximate Bayesian inferences with aghq using very little additional code. The aghq package is available from CRAN and complete code for all examples in this paper can be found at https://github.com/awstringer1/aghq-software-paper-code.