Modeling Correlated Arrival Events with Latent Semi-Markov Processes
Authors: Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | apply our ideas to both synthetic data and a real-world biometrics application. We evaluate the performance of our model and inference methodology on both synthetic and real-world biometrics data. |
| Researcher Affiliation | Collaboration | Wenzhao Lian WL89@DUKE.EDU Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA Vinayak Rao VAR11@STAT.DUKE.EDU Department of Statistical Science, Duke University, Durham, NC 27708, USA Brian Eriksson BRIAN.ERIKSSON@TECHNICOLOR.COM Technicolor Research Center, 735 Emerson Street, Palo Alto, CA 94301, USA Lawrence Carin LCARIN@DUKE.EDU Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA |
| Pseudocode | Yes | Algorithm 1 in the appendix gives details of this generative process. |
| Open Source Code | Yes | Code available at http://people.duke.edu/~wl89/ |
| Open Datasets | No | The paper uses synthetic data and a custom-collected biometrics dataset but does not provide concrete access information or state its public availability. |
| Dataset Splits | No | The paper describes how the synthetic data was generated and the MCMC iterations (e.g., 'discarding the first 2000 as burn-in'), but does not specify explicit training, validation, and test splits for the real-world dataset. |
| Hardware Specification | Yes | The running time of a typical trial (with T = 1000 and about 120 event arrivals for each user) was about 3000 seconds with unoptimized Matlab code on a computer with 2.2GHz CPU and 8GB RAM. |
| Software Dependencies | No | The paper mentions 'unoptimized Matlab code' but does not specify the version number of Matlab or any other software dependencies with their versions. |
| Experiment Setup | Yes | For inference, the fixed hyperparameters of the sampler were set as: α = 3, c = d = e = f = 10 3, and πk = [0.5, 0.5]T . We ran 5000 MCMC iterations of our MCMC sampler, discarding the first 2000 as burn-in, with posterior samples collected every 5 iterations. |