Bayesian Hierarchical Forecasting
Bayesian Hierarchical Forecasting is a hierarchical forecasting framework that uses Bayesian inference to model uncertainty and information sharing across multiple levels of a hierarchy, distinguishing itself by formally incorporating prior knowledge and probabilistic dependencies to improve forecast coherence and accuracy.
77
Publications
4.6K
Citations
272
Authors
194
Institutions
Publications per year
2017–2026
28
Parents
77
Customer Satisfaction, Cash Flow, and Shareholder Value
Thomas S. Gruca, Lopo L. Rego · Journal of Marketing · 2005 · 753 citations
Bayesian Probabilistic Projections of Life Expectancy for All Countries
Adrian E. Raftery, Jennifer Chunn, Patrick Gerland et al. · Demography · 2013 · 210 citations · Full text
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194
The Ohio State University
Columbus, United States
5 h-index
8 publications
534 citations

University of Bologna
Bologna, Italy
4 h-index
5 publications
152 citations
University of Missouri
Columbia, United States
4 h-index
6 publications
328 citations
United States Geological Survey
Reston, United States
3 h-index
6 publications
385 citations
University of Colorado Boulder
Boulder, United States
3 h-index
5 publications
138 citations

Monash University
Melbourne, Australia
3 h-index
3 publications
146 citations

Australian Bureau of Statistics
Belconnen, Australia
2 h-index
2 publications
72 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
3 publications
190 citations
1–8 of 194
4 publications · 78 citations
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3 publications · 425 citations
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3 publications · 84 citations
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3 publications · 145 citations
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3 publications · 53 citations
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