17 citations · 19 across the 5 of their papers we have counts for
5 papers
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…
Reply to Discussions of "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue"
Anna K. Yanchenko, Graham Tierney, Joseph Lawson +3
We are most grateful to all discussants for their positive comments and many thought-provoking questions. In addition, the discussants provide a number of useful leads into various…
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…
Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue
Anna K. Yanchenko, Graham Tierney, Joseph Lawson +3
Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on mod…
Reconstructing evolving signalling networks by hidden Markov nested effects models
Xin Wang, Ke Yuan, Christoph Hellmayr +2
Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direc…