Zobrazeno 1 - 10
of 616
pro vyhledávání: '"West, Mike"'
The macroeconomy is a sophisticated dynamic system involving significant uncertainties that complicate modelling. In response, decision makers consider multiple models that provide different predictions and policy recommendations which are then synth
Externí odkaz:
http://arxiv.org/abs/2406.03321
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for causal prediction. This extends the utility of existing models with computationally efficient methodology, enabling routine explorati
Externí odkaz:
http://arxiv.org/abs/2406.02320
Autor:
Tallman, Emily, West, Mike
We discuss and develop Bayesian dynamic modelling and predictive decision synthesis for portfolio analysis. The context involves model uncertainty with a set of candidate models for financial time series with main foci in sequential learning, forecas
Externí odkaz:
http://arxiv.org/abs/2405.01598
Reply to Discussions of 'Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue'
Autor:
Yanchenko, Anna K., Tierney, Graham, Lawson, Joseph, Hellmayr, Christoph, Cron, Andrew, West, Mike
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 areas of the literatures on time series, forecasting and commercial a
Externí odkaz:
http://arxiv.org/abs/2304.00552
Bayesian forecasting is developed in multivariate time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated units focuses on the impacts of interventions using contemporaneous outc
Externí odkaz:
http://arxiv.org/abs/2302.03200
Autor:
Tallman, Emily, West, Mike
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 the foundations and basic theory of ET for conditioning predictive di
Externí odkaz:
http://arxiv.org/abs/2207.10013
Autor:
Tallman, Emily, West, Mike
Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical founda
Externí odkaz:
http://arxiv.org/abs/2206.03815
Autor:
Yanchenko, Anna K., Tierney, Graham, Lawson, Joseph, Hellmayr, Christoph, Cron, Andrew, West, Mike
Publikováno v:
Applied Stochastic Models in Business and Industry, forthcoming, 2022
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 model developments to address these challenges for forecasting in a large
Externí odkaz:
http://arxiv.org/abs/2112.05678
We present a case study and methodological developments in large-scale hierarchical dynamic modeling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of customer/household-specific purchasing behav
Externí odkaz:
http://arxiv.org/abs/2101.03408