  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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