16 citations · 17 across the 9 of their papers we have counts for
11 papers
Comparing statistical learning models in wastewater-based epidemiology: An application to norovirus
Caelan McNamara, Fion Tan, Ella White +2
Wastewater-based epidemiology (WBE) is an increasingly important tool for infectious disease surveillance, but there has been limited direct comparison of modelling approaches for…
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Yahya Aalaila, Mouad Elhamdi, Gerrit Großmann +3
Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such reg…
Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes
Daniel Jenson, Jhonathan Navott, Piotr Grynfelder +4
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. While early architectures we…
Real-time small area estimation of food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification
Sahoko Ishida, Adam Howes, Valerie Bradley +14
Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key…
DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priors
Jhonathan Navott, Daniel Jenson, Seth Flaxman +1
Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their scaling makes them intractable for la…
Transformer Neural Processes - Kernel Regression
Daniel Jenson, Jhonathan Navott, Mengyan Zhang +3
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. Originally developed as a sc…
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Elizaveta Semenova, Swapnil Mishra, Samir Bhatt +2
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the st…
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming +3
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generato…
PriorVAE: Encoding spatial priors with VAEs for small-area estimation
Elizaveta Semenova, Yidan Xu, Adam Howes +4
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical m…
Bayesian workflow for disease transmission modeling in Stan
Léo Grinsztajn, Elizaveta Semenova, Charles C. Margossian +1
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and oth…
Strong continuity for the 2D Euler equations
Gianluca Crippa, Elizaveta Semenova, Stefano Spirito
We prove two results of strong continuity with respect to the initial datum for bounded solutions to the Euler equations in vorticity form. The first result provides sequential con…