Reviews: Tomography of the London Underground: a Scalable Model for Origin-Destination Data
–Neural Information Processing Systems
I thank the authors for the clarification in their rebuttal. It is even more clear that the authors should better contrast their work with aggregate approaches such as Dan Sheldon's collective graphical models (e.g., Sheldon and Dietterich (2011), Kumar et al. 2013, Bernstein and Sheldon 2016). Part of the confusion came from some of the modeling choices: In equation (1) the travel times added by one station is Poisson distributed?! Poisson is often used for link loads (how many people there are in a given station), not to model time. Is the quantization of time too coarse for a continuous-time model? Wouldn't a phase-type distribution(e.g., Erlang) be a better choice for time? Such modeling choices must be explained.
Neural Information Processing Systems
Oct-8-2024, 07:13:08 GMT
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- Europe > United Kingdom > England > Greater London > London (0.40)
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