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The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involvessampling from conditional densities of utility parameters usingMetropolis-Hastings (MH) algorithm due to unavailability of conjugate prior forlogit kernel. To address this non-conjugacy concern, we propose the applicationof P\'olygamma data augmentation (PG-DA) technique for the MMNL estimation. Theposterior estimates of the augmented and the default Gibbs sampler are similarfor two-alternative scenario (binary choice), but we encounter empiricalidentification issues in the case of more alternatives ($J \geq 3$).

P\'olygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models