117 citations · 522 across the 22 of their papers we have counts for
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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…
On Entropic Tilting and Predictive Conditioning
Emily Tallman, Mike West
Entropic tilting (ET) is a Bayesian decision-analytic method for constraining distributions to satisfy defined targets or bounds for sets of expectations. This report recapitulates…
Bayesian Predictive Decision Synthesis
Emily Tallman, Mike West
Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing and combining inferences from sets of models. Bayesian predictiv…
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…
Hierarchical Dynamic Modeling for Individualized Bayesian Forecasting
Anna K. Yanchenko, Di Daniel Deng, Jinglan Li +2
We present a case study and methodological developments in large-scale hierarchical dynamic modeling for personalized prediction in commerce. The context is supermarket sales, wher…