4 citations · 10 across the 8 of their papers we have counts for
8 papers
FairBED: Incorporating Fairness into Bayesian Experimental Design
Marcel Hedman, Emily Alger, Brieuc Lehmann +2
Bayesian experimental design (BED) has traditionally focused exclusively on collecting data that is maximally informative, with little consideration of other desirable dataset char…
Learning from data with structured missingness
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti +17
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. H…
Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates
Sahra Ghalebikesabi, Chris Holmes, Edwin Fong +1
Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior. In the context of density estimation, t…
Neural Score Matching for High-Dimensional Causal Inference
Oscar Clivio, Fabian Falck, Brieuc Lehmann +2
Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact…
A Predictive Approach to Bayesian Nonparametric Survival Analysis
Edwin Fong, Brieuc Lehmann
Bayesian nonparametric methods are a popular choice for analysing survival data due to their ability to flexibly model the distribution of survival times. These methods typically e…
Interoperability of statistical models in pandemic preparedness: principles and reality
George Nicholson, Marta Blangiardo, Mark Briers +12
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving p…
Bayesian exponential random graph models for populations of networks
Brieuc Lehmann, Simon White
The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. A canonic…
Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag
Radka Jersakova, James Lomax, James Hetherington +4
Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In th…