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..
Statistical and Computational Guarantees for the Baum-Welch Algorithm
Authors: Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with thorough numerical simulations studying the convergence of the Baum-Welch algorithm and illustrating the accuracy of our predictions. |
| Researcher Affiliation | Academia | Fanny Yang EMAIL Department of Electrical Engineering and Computer Sciences University of California Berkeley, CA 94720-1776, USA; Sivaraman Balakrishnan EMAIL Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213, USA; Martin J. Wainwright EMAIL Department of Statistics Department of Electrical Engineering and Computer Sciences University of California Berkeley, CA 94720-1776, USA |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. The algorithms are described in narrative text. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There is no explicit statement of code release, nor a link to a code repository. |
| Open Datasets | No | The paper uses generated data for simulations, specifically a 'two-state Gaussian output HMMs' and 'a fixed sample sequence Xn 1 drawn from a model' for evaluation. No publicly available or open dataset is mentioned with concrete access information. |
| Dataset Splits | No | The paper conducts simulations by generating data from a model rather than using external datasets. Therefore, it does not provide specific training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. The 'Simulations' section (4.3) describes the experimental results but omits information on the computational hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In all simulations, we fix the mixing parameter to ρmix = 0.6, generate initial vectors bµ0 randomly in a ball of radius r : = µ 2 /4 around the true parameter µ , and set bζ0 = 1 /2. |