A comparison of variational approximations for fast inference in mixed logit models

  title={A comparison of variational approximations for fast inference in mixed logit models},
  author={Nicolas Depraetere and Martina Vandebroek},
  journal={Computational Statistics},
Variational Bayesian methods aim to address some of the weaknesses (computation time, storage costs and convergence monitoring) of mainstream Markov chain Monte Carlo based inference at the cost of a biased but more tractable approximation to the posterior distribution. We investigate the performance of variational approximations in the context of the mixed logit model, which is one of the most used models for discrete choice data. A typical treatment using the variational Bayesian methodology… Expand
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  • 2019 International Joint Conference on Neural Networks (IJCNN)
  • 2019
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