摘要翻译：
变分贝叶斯(VB)是一种起源于机器学习的方法，能够快速、可扩展地估计复杂的概率模型。到目前为止，VB在离散选择分析中的应用仅限于未观察到个体间口味异质性的混合logit模型。然而，这种模型在面板数据设置中可能过于严格，因为个人之间以及同一个人遇到的不同选择任务之间的品味可能会有所不同。本文给出了一种VB方法，用于在未观察到的个体间和个体内异质性的混合logit模型中进行后验推断。在仿真研究中，我们从参数恢复、预测精度和计算效率等方面比较了所提出的VB方法与最大模拟似然(MSL)和马尔可夫链蒙特卡罗(MCMC)方法的性能。仿真研究表明，VB可以作为MSL和MCMC估计的一种快速、可扩展和准确的替代方法，特别是在快速预测非常重要的应用中。VB被观察到比两种竞争方法快2.8到17.7倍，同时提供了可比或更高的准确性。此外，仿真研究表明，用解析梯度并行实现MSL估计器在估计精度和计算效率方面是MCMC的一个可行的替代方案，因为MSL估计器比MCMC快0.9～2.1倍。
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英文标题：
《Variational Bayesian Inference for Mixed Logit Models with Unobserved
  Inter- and Intra-Individual Heterogeneity》
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作者：
Rico Krueger, Prateek Bansal, Michel Bierlaire, Ricardo A. Daziano,
  Taha H. Rashidi
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Variational Bayes (VB), a method originating from machine learning, enables fast and scalable estimation of complex probabilistic models. Thus far, applications of VB in discrete choice analysis have been limited to mixed logit models with unobserved inter-individual taste heterogeneity. However, such a model formulation may be too restrictive in panel data settings, since tastes may vary both between individuals as well as across choice tasks encountered by the same individual. In this paper, we derive a VB method for posterior inference in mixed logit models with unobserved inter- and intra-individual heterogeneity. In a simulation study, we benchmark the performance of the proposed VB method against maximum simulated likelihood (MSL) and Markov chain Monte Carlo (MCMC) methods in terms of parameter recovery, predictive accuracy and computational efficiency. The simulation study shows that VB can be a fast, scalable and accurate alternative to MSL and MCMC estimation, especially in applications in which fast predictions are paramount. VB is observed to be between 2.8 and 17.7 times faster than the two competing methods, while affording comparable or superior accuracy. Besides, the simulation study demonstrates that a parallelised implementation of the MSL estimator with analytical gradients is a viable alternative to MCMC in terms of both estimation accuracy and computational efficiency, as the MSL estimator is observed to be between 0.9 and 2.1 times faster than MCMC.
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PDF链接：
https://arxiv.org/pdf/1905.00419