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A nonparametric Bayesian approach is proposed to estimate an integrated travel demand model of work duration and commute mode. The proposed nonparametric model, a generalized additive Gaussian process model, precludes the need to prespecify a functional form. The additive structure of the model enables computational tractability in high dimensions of covariates. Bayesian estimation was adopted for inference to quantify uncertainty using probability distributions.
In Mobil.TUM

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  • An Integrated Travel Demand Model of Work Duration and Commute-mode Choices: A Flexible Modeling Approach

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Last month, I gave a presentation titled Introduction to Probabilistic Machine Learning using PyMC3 at two local meetup groups (Bayesian Data Science D.C. and Data Science & Cybersecurity) in McLean, Virginia. The following is a summary of the concepts we discussed regarding Principled AI.




Pymc-learn is a library for practical probabilistic machine learning in Python. It provides a variety of state-of-the art probabilistic models for supervised and unsupervised machine learning. It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a syntax that mimics scikit-learn. Emphasis is put on ease of use, productivity, flexibility, & performance.

Automating Hyperparameter tuning

Bayesian optimization provides a principled approach to automate hyperparameter tuning.

Integrated Travel Demand Modeling

Currently, there’s growing research interest in integrated travel demand models.

Latent Travel Patterns from Mobile Phone Data

Currently, there’s growing research interest in integrated travel demand models.