Efficient Continuous-Time Markov Chain Estimation
Authors: Monir Hajiaghayi, Bonnie Kirkpatrick, Liangliang Wang, Alexandre Bouchard-Côté
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These experiments are performed on both synthetic and real datasets, drawing from two important examples of CTMCs having combinatorial state spaces: string-valued mutation models in phylogenetics and nucleic acid folding pathways. |
| Researcher Affiliation | Academia | Monir Hajiaghayi MONIRH@CS.UBC.CA Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada Bonnie Kirkpatrick BBKIRK@CS.MIAMI.EDU Department of Computer Science, University of Miami, Coral Gables, FL 33124, United States Liangliang Wang LIANGLIANG WANG@SFU.CA Department of Statistical and Actuarial Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada Alexandre Bouchard-Cˆot e BOUCHARD@STAT.UBC.CA Statistics Department, University of British Columbia, Vancouver, BC V6T 1Z4, Canada |
| Pseudocode | Yes | See Algorithm 1 in the Supplement for details. [...] See Algorithm 4 in the Supplement for details. [...] we also give in Algorithm 6 the details of how we combined our method with phyloge- |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper mentions generating synthetic datasets and using RNA molecules from a supplement table, but it does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper only mentions 'CPU time' and 'computational budget' without providing specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters used for the TIPS method are as follows: α = 2/3 and β = max(0.25, 1/T * 1/6) where T is the specified folding time interval. |