Learning Parametric-Output HMMs with Two Aliased States

Authors: Roi Weiss, Boaz Nadler

ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We illustrate our theoretical analysis by several simulations. and 6. Numerical simulations The following simulation results illustrate the consistency of our methods to detect aliasing, identify the aliased component and learn the transition matrix A.
Researcher Affiliation Academia Roi Weiss ROIWEI@CS.BGU.AC.IL Department of Computer Science, Ben-Gurion University, Beer Sheva, 84105, Israel. Boaz Nadler BOAZ.NADLER@WEIZMANN.AC.IL Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, 76100, Israel.
Pseudocode Yes In the process, we provide a simple procedure (Algorithm 1) to determine whether a given minimal 2A-HMM is identifiable or not.
Open Source Code No The paper does not provide any statement or link regarding the availability of open-source code for the described methodology.
Open Datasets No The paper states: "Let (Yt)T 1 t=0 be an output sequence generated by a parametric-output HMM...". This indicates simulated data, not a publicly available dataset with concrete access information (link, DOI, citation).
Dataset Splits No The paper does not specify any dataset splits (e.g., train/validation/test percentages or sample counts), nor does it reference predefined splits with citations for reproducibility.
Hardware Specification No The paper does not specify any hardware details, such as CPU or GPU models, memory, or specific computing environments (e.g., cloud instances), used for running the experiments.
Software Dependencies No The paper does not provide specific software names with version numbers or list any key libraries or solvers used in the implementation.
Experiment Setup Yes The paper states: "In both figures, the number of iterations of the BW was set to 20.", which is a specific experimental setting.