I develop novel methods for fitting complex models to large datasets using Bayesian inference. In my doctoral work, I developed an inference method for Extended Latent Gaussian Models (ELGMs), and demonstrated their application in spatial epidemiology, astrophysics, and other areas.
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!
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]
A. Stringer, P. Brown, J. Stafford. (2020) Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models. arXiv:2103.07425 [stat.ME]
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 (2021). Implementing approximate Bayesian inference using adaptive quadrature: the aghq package
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) (November 2021)
Statistical Society of Canada annual conference (June 2021)
A. Stringer (2020). Smooth estimation of nonlinear rate curves using Bayesian inference.
A. Stringer (with P. Brown and J. Stafford) (2020). Approximate Bayesian inference for Case Crossover models.
Canadian Statistics Student Conference. Ottawa, Canada (research presentation).