On Learning Markov Chains
Authors: Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We augment the theory with experiments that demonstrate the efficacy of our proposed estimators and validate the functional form of the derived bounds. |
| Researcher Affiliation | Academia | Yi HAO Dept. of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 yih179@ucsd.edu Alon Orlitsky Dept. of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 alon@ucsd.edu Venkatadheeraj Pichapati Dept. of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 dheerajpv7@ucsd.edu |
| Pseudocode | No | The paper provides mathematical definitions of estimators but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the availability of source code or links to a code repository. |
| Open Datasets | No | The paper describes a process for generating synthetic data for experiments (e.g., 'drawn from the k-Dirichlet(1) distribution', 'construct a transition matrix M'), but does not provide concrete access information (link, DOI, formal citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes parameters for data generation (e.g., '10,000 <= n <= 100,000'), but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or versions (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | For the first three figures, k = 6, δ = 0.05, and 10, 000 n 100, 000. For the last figure, δ = 0.01, n = 100, 000, and 4 k 36. In all the experiments, the initial distribution µ of the Markov chain is drawn from the k-Dirichlet(1) distribution. For the transition matrix M, we first construct a transition matrix M where each row is drawn independently from the k-Dirichlet(1) distribution. To ensure that each element of M is at least δ, let Jk represent the k k all-ones matrix, and set M = M (1 kδ) + δJk. We generate a new Markov chain for each curve in the plots. And each data point on the curve shows the average loss of 100 independent restarts of the same Markov chain. |