Prateek Bansal, Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, Taha H. Rashidi
arXiv 13 Apr 2019 · Statistics — Machine Learning
arXiv:1904.07688 · PDF · DOI · OpenAlex · Extracted main text
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 P\'olygamma 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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Akinc, D. and Vandebroek, M (2018) Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix | 0.737 | 3 | 2 | 100% |
| 2 | Polson, N. G., Scott, J. G., and Windle, J (2013) Bayesian inference for logistic models using pólya–gamma latent variables | 0.585 | 3 | 1 | 100% |
| 3 | Huang, A. and Wand, M. P (2013) Simple marginally noninformative prior distributions for covariance matrices | 0.511 | 2 | 1 | 100% |
| 4 | Holmes, C. C., Held, L., et al (2006) Bayesian auxiliary variable models for binary and multinomial regression | 0.405 | 1 | 1 | 100% |
| 5 | Linderman, S., Johnson, M., and Adams, R. P (2015) Dependent multinomial models made easy: Stick-breaking with the pólya-gamma augmentation | 0.405 | 1 | 1 | 100% |
| 6 | McFadden, D. and Train, K (2000) Mixed MNL models for discrete response | 0.405 | 1 | 1 | 100% |
| 7 | Tan, L. S. L (2017) Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes | 0.405 | 1 | 1 | 100% |
| 8 | Train, K. E (2009) Discrete Choice Methods with Simulation | 0.405 | 1 | 1 | 100% |
| 9 | Zhang, Q. and Zhou, M (2017) Permuted and augmented stick-breaking bayesian multinomial regression | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.