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arXiv · 1904.07688

Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models

Prateek Bansal·Rico Krueger·Michel Bierlaire·Ricardo A. Daziano·Taha H. Rashidi
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Abstract

The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this non-conjugacy concern, we propose the application of Pólygamma data augmentation (PG-DA) technique for the MMNL estimation. The posterior estimates of the augmented and the default Gibbs sampler are similar for two-alternative scenario (binary choice), but we encounter empirical identification issues in the case of more alternatives ($J \geq 3$).

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Prateek Bansal, Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, Taha H. Rashidi. 2019-04-13. Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models. https://arxiv.org/abs/1904.07688

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