A dynamic product-level factor model with Bayesian variational inference improves predictive accuracy for consumer demand in high-dimensional retail settings by capturing inertia and correlated heterogeneity.
Journal of Machine Learning Research , volume=
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Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.
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Dynamic Consumer Demand at Large Scale
A dynamic product-level factor model with Bayesian variational inference improves predictive accuracy for consumer demand in high-dimensional retail settings by capturing inertia and correlated heterogeneity.
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On Bayesian Softmax-Gated Mixture-of-Experts Models
Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.