Exploiting locality in high-dimensional Factorial hidden Markov models
Authors: Lorenzo Rimella, Nick Whiteley
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the new algorithms on synthetic examples and a London Underground passenger flow problem, where the factor graph is effectively given by the train network. |
| Researcher Affiliation | Academia | Lorenzo Rimella EMAIL Department of Mathematics and Statistics Lancaster University Lancaster, LA1 4YF, UK Nick Whiteley EMAIL Institute for Statistical Science School of Mathematics University of Bristol Bristol, BS8 1TW, UK and the Alan Turing Institute, UK |
| Pseudocode | Yes | Algorithm 1 Approximate Bayes update |
| Open Source Code | Yes | All the codes are implemented in Python 3 and available on Github (Online code). |
| Open Datasets | Yes | Transport For London, the operator of the London Underground, has made publicly available tap data, consisting of a 5% sample of all Oyster card journeys in a week during November 2009 (Transport for London, 2018). |
| Dataset Splits | Yes | The data are split into training given by Monday, Tuesday and Wednesday and test consisting of Thursday and Friday. |
| Hardware Specification | Yes | We used the University of Bristol s Blue Crystal High Performance Computing machine. The experiments were run on either one or two standard compute nodes each with 2 x 2.6GHz 8-core Intel E5-2670 (Sandy Bridge) chips and 4GB of RAM per core. |
| Software Dependencies | No | All the codes are implemented in Python 3 and available on Github (Online code). Only a programming language with a version (Python 3) is mentioned, without specific versioned libraries or solvers. |
| Experiment Setup | Yes | We took X t0, 1u and simulated three data sets of length T 500 from the model with parameters: ˆµ0pxvq 1, xv 1, @v P V, tˆppxv, zvquxv,zv PX ˆ0.6 0.4 0.2 0.8 , c 1, σ2 1. |