1 research outputs found
P\'olygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
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 ().Comment: arXiv admin note: text overlap with arXiv:1904.0364