Statistical and Computational Guarantees for the Baum-Welch Algorithm
Authors: Fanny Yang, Sivaraman Balakrishnan, Martin J. Wainwright
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | 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. |