AI 中文总结
本研究比较贝叶斯机器学习方法（潜在因子模型与混合logit模型，MCMC与VI）用于大规模多类别需求估计，发现潜在因子模型随维度增加性能提升，MCMC精度最高但计算昂贵，VI在精度略降下大幅提速并优于混合logit。
AI 中文摘要
本研究探讨了贝叶斯机器学习方法如何用于具有多个产品类别的大规模需求估计。我比较了两类模型，即潜在因子模型和混合logit模型，以及两种贝叶斯估计方法，即马尔可夫链蒙特卡洛（MCMC）和变分推断（VI）。分析结合了模拟研究和超市扫描数据的应用。结果表明，潜在因子模型受益于跨类别的信息，并随着选择环境维度的增加而提高其预测性能，而混合logit模型则没有表现出相同的模式。MCMC提供了最高的预测准确性，但计算密集。VI的预测性能略低，但大幅减少了运行时间。在实证应用中，VI也优于混合logit基准。这些发现凸显了在多类别需求估计中准确性与计算可行性之间的权衡。
英文摘要
This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from information across categories and improves its predictive performance as the dimensionality of the choice environment increases, whereas the mixed logit model does not exhibit the same pattern. MCMC delivers the highest predictive accuracy but is computationally intensive. VI achieves slightly lower predictive performance while substantially reducing runtime. In the empirical application, VI also outperforms the mixed logit benchmark. These findings highlight a trade-off between accuracy and computational feasibility in multi-category demand estimation.
