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Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

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Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

P. Bansal, R. Krueger, et al.

Discover how Variational Bayes (VB) methods provide a faster and more efficient alternative to traditional Markov chain Monte Carlo (MCMC) methods in estimating mixed multinomial logit models. This groundbreaking research by Prateek Bansal, Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, and Taha H. Rashidi reveals enhancements to VB methods and compares their performance with MCMC and MSLE, showing significant speed advantages.

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