I develop novel methods for fitting complex models to large datasets using Bayesian inference. In my doctoral work, I have introduced a novel class of models, Extended Latent Gaussian Models (ELGMs), and demonstrated their application in spatial epidemiology, astrophysics, and other areas.

I am currently working in the following areas:

  • Bayesian inference using adaptive quadrature: fast, scalable, MCMC-free approximate Bayesian inference with higher-order asymptotic accuracy;

  • Skew-corrected approximate Bayesian inference: highly accurate multivariate Skew-normal approximations which will give better uncertainty quantification in complex models for spatio-temporal data, among others;

  • Approximate Bayesian inference for massive Extended Latent Gaussian models: using automatic differentiation to reduce the memory requirements associated with fitting Extended Latent Gaussian models, and then scaling this methodology to models and/or datasets with billions of parameters and/or data points, like those seen in astrophysics and the modelling of global climate change dynamics.

Note: If you're looking at the presentations below, you should view them in "presentation" mode, because I make (way too) heavy use of the "pause" feature in Beamer, which only really shows up well in presentation mode. And the "handout" option which removes the pauses obscures some figures. Sorry!

Selected publications

  • A. Stringer, P. Brown, J. Stafford. (2020) Approximate Bayesian Inference for Case Crossover Models. Biometrics, to appear.


  • A. Stringer (2021): Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package. arXiv:2101.04468 [stat.CO]

  • B. Bilodeau*, A. Stringer*, Y. Tang* (2021). Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference. arXiv:2102.06801 [stat.ME]

Working papers

  • A. Stringer, P. Brown, J. Stafford. (2020) Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models. In preparation

  • Z. Zhang, A. Stringer, P. Brown, J. Stafford. (2020) Bayesian inference for Cox Proportional Hazard Models with Partial Likelihoods, Semi-parametric Covariate Effects and Correlated Observations. Submitted

  • A. Stringer, Z. Zhang, P. Brown, J. Stafford (2020). Proper Approximations to Random Walk Priors for Bayesian Smoothing. In preparation.


  • A. Stringer (with P. Brown and J. Stafford) (2020). “Bayesian inference for Extended Latent Gaussian Models”. l’Ecole Polytechnique Federale de Lausanne. Lausanne, Switzerland (invited research presentation).

  • A. Stringer (2020). Smooth estimation of nonlinear rate curves using Bayesian inference. University of California, Berkeley. Berkeley, USA (invited research presentation).

  • A. Stringer (with P. Brown and J. Stafford) (2020). Approximate Bayesian inference for Case Crossover models. Canadian Statistics Student Conference. Ottawa, Canada (research presentation).