3 citations · 7 across the 7 of their papers we have counts for
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stat.ME2024
Compositional dynamic modelling for causal prediction in multivariate time series
Kevin Li, Graham Tierney, Christoph Hellmayr +1
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for causal prediction. This extends the utility of existing models…
stat.ME2023★ 1 cited
ProSpar-GP: scalable Gaussian process modeling with massive non-stationary datasets
Kevin Li, Simon Mak
Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has bee…
stat.ME2023★ 1 cited
Multivariate Bayesian dynamic modeling for causal prediction
Graham Tierney, Christoph Hellmayr, Greg Barkimer +2
Bayesian forecasting is developed in multivariate time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated un…