The number of distinct adjacent pairs in geometrically distributed
words: a probabilistic and combinatorial analysisArticle
Authors: Guy Louchard ; Werner Schachinger ; Mark Daniel Ward
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Guy Louchard;Werner Schachinger;Mark Daniel Ward
The analysis of strings of $n$ random variables with geometric distribution has recently attracted renewed interest: Archibald et al. consider the number of distinct adjacent pairs in geometrically distributed words. They obtain the asymptotic ($n\rightarrow\infty$) mean of this number in the cases of different and identical pairs. In this paper we are interested in all asymptotic moments in the identical case, in the asymptotic variance in the different case and in the asymptotic distribution in both cases. We use two approaches: the first one, the probabilistic approach, leads to variances in both cases and to some conjectures on all moments in the identical case and on the distribution in both cases. The second approach, the combinatorial one, relies on multivariate pattern matching techniques, yielding exact formulas for first and second moments. We use such tools as Mellin transforms, Analytic Combinatorics, Markov Chains.
Comment: 47 pages, 4 figures
Volume: vol. 25:2
Section: Combinatorics
Published on: October 2, 2023
Accepted on: June 29, 2023
Submitted on: April 3, 2022
Keywords: Mathematics - Probability, 05A16, 60C05, 60F05
Funding:
Source : OpenAIRE Graph- MCTP: Sophomore Transitions: Bridges into a Statistics Major and Big Data Research Experiences via Learning Communities; Funder: National Science Foundation; Code: 1246818
- Emerging Frontiers of Science of Information; Funder: National Science Foundation; Code: 0939370
- Category I: Anvil - A National Composable Advanced Computational Resource for the Future of Science and Engineering; Funder: National Science Foundation; Code: 2005632
- HDR DSC: National Data Mine Network; Funder: National Science Foundation; Code: 2123321
- HDR Institute: Geospatial Understanding through an Integrative Discovery Environment; Funder: National Science Foundation; Code: 2118329
- NSF Convergence Accelerator Track H: Developing Experiential Accessible Framework for Partnerships and Opportunities in Data Science (for the deaf community); Funder: National Science Foundation; Code: 2235473