Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes
Authors: E. Busra Celikkaya, Christian Shelton
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our method, TTOP (Tree of Time Ordered Products), as part of the CTBN-RLE code base (Shelton et al., 2010), and it will be included in the next version. We evaluated our method on a synthetic network of Ising model dynamics. |
| Researcher Affiliation | Academia | E. Busra Celikkaya EMAIL University of California, Riverside Christian R. Shelton EMAIL University of California, Riverside |
| Pseudocode | Yes | Algorithm 1 TTOP Filter |
| Open Source Code | No | We implemented our method, TTOP (Tree of Time Ordered Products), as part of the CTBN-RLE code base (Shelton et al., 2010), and it will be included in the next version. |
| Open Datasets | No | We evaluated our method on a synthetic network of Ising model dynamics. The Ising model was chosen so that we could compute the true answer in a reasonable time and scale the problem size. Using this model, we generated a directed toroid network structure with cycles following (El-Hay et al., 2010). |
| Dataset Splits | No | The paper uses a synthetic network and does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or types) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions implementing the method as part of the 'CTBN-RLE code base' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For TTOP, we set the number of splits for the quadrature (see Equation 5) to 10 because it produces a good computation time versus error performance. ... For Aux Gibbs, we vary the sample size between 50 and 5000, and set the burn-in period to be 10% of this value. For IS, the sample size varies between 500 to 50000. |