Variational Bayes (VB) methods have emerged as a fast and computationally-efficient
alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation …

Variational inference methods have been shown to lead to significant improvements in the
computational efficiency of approximate Bayesian inference in mixed multinomial logit …

Variational Bayes (VB), a method originating from machine learning, enables fast and
scalable estimation of complex probabilistic models. Thus far, applications of VB in discrete …

Discrete choice models describe the choices made by decision makers among alternatives
and play an important role in transportation planning, marketing research and other …

Variational Bayesian methods aim to address some of the weaknesses (computation time,
storage costs and convergence monitoring) of mainstream Markov chain Monte Carlo based …

Statisticians along with other scientists have made significant computational advances that
enable the estimation of formerly complex statistical models. The Bayesian inference …

The multinomial probit model is often used to analyze choice behavior. However, estimation
with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which …

Discrete choice models are commonly used by applied statisticians in numerous fields, such
as marketing, economics, finance, and operations research. When agents in discrete choice …

Motivated by the promising performance of alternative estimation methods for mixed logit
models, in this paper we derive, implement, and test minorization-maximization (MM) …

Estimating discrete choice models on panel data allows for the estimation of preference
heterogeneity in the sample. While the Logit Mixture model with random parameters is …