Mixture Model Averaging
Mixture Model Averaging is a Bayesian model averaging technique that combines predictive distributions from multiple candidate models using a mixture distribution over model-specific parameters, where the mixing weights are determined by posterior model probabilities, thereby accounting for model uncertainty in inference and prediction.
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79
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8
Authors
9
Institutions
Publications per year
2017–2026
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DETECTING FINANCIAL DATA DEPENDENCE STRUCTURE BY AVERAGING MIXTURE COPULAS
Guannan Liu, Wei Long, Xinyu Zhang et al. · Econometric Theory · 2018 · 17 citations
Mixture Model Averaging for Clustering
2012 · 16 citations
Forecasting product sales with dynamic linear mixture models
Phillip M. Yelland, Eunice Lee · 2003 · 12 citations
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Xiamen University
Xiamen, China
1 h-index
1 publications
17 citations

Tulane University
New Orleans, United States
1 h-index
1 publications
17 citations
Capital University
Bexley, United States
1 h-index
1 publications
17 citations

McMaster University
Hamilton, Canada
1 h-index
1 publications
19 citations

University of Auckland
Auckland, New Zealand
1 h-index
2 publications
30 citations
University of Guelph
Guelph, Canada
1 h-index
1 publications
19 citations

Qingdao University
Qingdao, China
1 h-index
1 publications
17 citations
Texas A&M University
College Station, United States
1 h-index
1 publications
17 citations
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1 publications · 15 citations
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1 publications · 17 citations
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1 publications · 19 citations
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