Amortized-MXL: Scaling Bayesian Inference of Mixed Multinomial Logit Models to Large Datasets
This repository contains source code for the Amortized Variational Inference approach for Mixed Multinomial Logit models proposed in:
- Rodrigues, F. Scaling Bayesian inference of mixed multinomial logit models to large datasets. In Transportation Research Part B: Methodological, 2022 (preprint version: https://arxiv.org/abs/2004.05426/)
The folder "v1.0" contains the latest version of the code, which includes an efficient implementation in pure PyTorch, an easy-to-use formula interface for specifying utility functions and tutorials on how to use it. See for example the Jupyter notebook: Demo - Simulated N=500 - MXL - SVI.ipynb.
The original code from the paper uses Pyro and is available in the folder "v0.1".