37 citations · 101 across the 12 of their papers we have counts for
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Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire +1
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying t…
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity
Rico Krueger, Prateek Bansal, Michel Bierlaire +2
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 c…
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due…
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed…